Problems of Information Society, 2015, №2 PROBLEMS of INFORMATION

Problems of information society, 2015, №2 PROBLEMS OF INFORMATION

SOCIETY

AZERBAIJAN NATIONAL ACADEMY OF SCIENCES INSTITUTE OF INFORMATION TECHNOLOGY

Scientific-practice journal

PROBLEMS OF INFORMATION SOCIETY

PROCEEDINGS OF INSTITUTE OF INFORMATION TECHNOLOGY of Azerbaijan National Academy of Sciences

Publisher: Azerbaijan National Aademy of Sciences Institute of Information Technology

Editor-in-Chief: Academic Rasim Alguliyev

Honorary Editor: Professor, Lotfi A. Zadeh

Editorial Board: Academic Ali Abbasov, Academic Isa Habibbayli, DrSc, professor Bakhtiyar Aliyev, DrSc, professor Azhdar Aghayev, DrSc, professor Rasim Hasanov, DrSc, professor Latif Huseynov, DrSc, professor Fuad Mammadov, DrSc, professor Rustam Mammadov, DrSc, professor Efendi Nasibov, DrSc, professor Masuma Mammadova, DrSc, professor Ilham Mammadzadeh, DrSc, professor Rena Mirzazadeh, DrSc, professor Ertekin Salamzadeh, DrSc, professor Vilayat Veliyev, DrSc Ramiz Aliguliyev, DrSc Nizami Gasilov, PhD, associated professor Zarifa Jabrayilova, PhD, associated professor Alireza Khastan, PhD, associated professor Shahin Amrahov, PhD, professor Seung-Woo Seo, professor Valentina Dagiene, PhD Rashid Alakbarov, PhD, associated professor Alovsat Aliyev, PhD Yadigar Imamverdiyev, PhD Norisma Binti Idris, Ph.D, assistant professor Raif Rustamov, PhD Asad Abdi, PhD Jasarat Valehov, PhD Farhad Yusifov.

Executive editor: Rasim Mahmudov

The journal was included into the "List of periodic scientific publications recommended for the publication of main contributions of theses in the Republic of Azerbaijan" by the decision of the Supreme Attestation Commission under the President of the Republic of Azerbaijan.

Problems of informationCONTENTS society , 2015, №2, 4-12

1. Rasim M. Aliguliyev, Rasim Sh. Mahmudov Multidisciplinary scientific-theoretical 3 problems of formation of information society……………………………………………. 2. Yadigar N. Imamverdiyev Social media and security concerns …...... 18

3. Rashid G. Alakbarov Supercomputers: current status and development prospects..…….. 24

4. Rasim M. Aliguliyev, Makrufa Sh. Hajirahimova, Aybaniz S. Aliyeva Current scientific and theoretical problems of big data……………………………………………………… 34 5. Alovsat G. Aliyev, Roza O. Shahverdiyeva An analytical examination of international recommendations on strategic guidelines and priority areas for the creation of innovative enterprises ……………………...………………………………………………………… 46 6. Zarifa G. Jabrayilova Formation of human resources for e-health: international experience, solutions and perspectives………………………………………..………..… 57 7. Afruz M. Gurbanova Development of terminological information system in the subject field…………………………………...... 69

8. Ramiz H. Shikhaliyev Security issues in social networks..………………………………. 74 9. Kamala K. Hashimova Prospects of advertising-marketing optimization of the websites during the search……………………………………………………………………...…... 82 10. Kamila A. Valiyeva Modern areas of computational linguistics.…...……………………. 91

2 www.jpis.az

Problems of information society, 2016, №2, 3-17

Rasim M. Aliguliyev 1, Rasim Sh. Mahmudov2 Institute of Information Technology of ANAS, Baku, Azerbaijan [email protected],[email protected] MULTIDISCIPLINARY SCIENTIFIC-THEORETICAL PROBLEMS OF FORMATION OF INFORMATION SOCIETY The article explores and classifies multidisciplinary problems of the formation of information society. Various viewpoints, approaches, concepts on the theory of information society are analyzed. Activities, calls of international organizations on building information society are discussed. Areas of activity of influential scientific research centers of the world and journals dealing with multidisciplinary problems of information society are studied. Key words: information society, post-industrial society, multidisciplinary approach, information policy, information production, innovative activity, intellectual property. Introduction Formation and development of information society which is the modern phase of development of civilisation is based on information and communication technologies (ICT). Therefore, ICT determines the characteristics and development trends of information society. Today ICT experiences comprehensive and dynamic development process. All the achievements in the ICT sector are of practical importance and these achievements are applied to all the spheres of society in a flexible manner. Hence, the society and human life undergo severe, in some cases unexpected and unpredictable transformations period today. Thus, interrelated, interacting and interlinked change process is observed in sociopolitical, socio-economic, cultural spheres. Since the information society is a production of complex changes appearing in all spheres of human activities, study of its scientific problems also requires multidisciplinary approach. In other words, scientific problems of information society requires coordinated studies on technology, economics, law, political science, philosophy, sociology, psychology, pedagogy, cultural studies, linguistics and other spheres. The research of scientific problems of information society began in the early 70s. A range of scientific concepts, theories, and approaches were put forth during the past period. In those studies the information society are expounded from various aspects. A number of international and regional organizations, as well as some agencies under the umbrella of the United Nations, apply the multidisciplinary approach while conducting researches, making reports, recommendations and concepts on information society. This approach of such organizations is also reflected in the national conceptual documents on the formation and development of information society. There are influential scientific research centers in various countries of the world doing research on problems of information society. Researches conducted in such centers also feature the multidisciplinary character are, and complex approach on problems of information society is applied. Aforementioned scientific centers prefer international cooperation and carry out various projects in this direction. Simultaneously, a range of highly rated scientific journals on multidisciplinary problems of information society are being published. The study and analysis of activities of relevant international and regional organizations, leading scientific centers and journals is of great significance for the institutions and researchers doing research on the problems of information society in our country. Scientific-theoretical viewpoints on information science Currently, there are lots of concepts characterizing information science. Authors of relevant concepts study most various aspects of information science. Some authors explore

www.jpis.az 3 Problems of information society, 2016, №2, 3-17 information science on the basis of multidisciplinary approach, i.e. from various scientific aspects, while others pay attention to its particular aspects. Those fundamental studies contain detailed scientific-theoretical analysis of new realities brought by ICT. In the 70s, there was a convergence of two ideologies that emerged almost in the same period – “post-industrial society” and “information society” ideologies. After this, “post- industrial society” and “information society” ideologies were used synonymously. It is generally accepted that the term of “information society” was included to the terminology by a professor of Tokio Institute of Technology, Y.Hayashi. The term was used by K.Katoyamı, T.Umesso, F.Machlup afterwards. In the reports, presented to the Japanese government with the title “Counters (outlines) of political aid to informatization of Japanese society” of the Council of Agricultural Structure (1969); “Information society plan” of the Institute of Utilizing Computer (1971); “Japanese information society: topic and approaches” of the Economic Planning Agency, general visions on the information society of the future were shaped: the development of computerization would provide access to reliable information resources for the people, and would set free them from routine by enabling the production to be highly automatized [4]. The general characteristics of information society were mentioned in such documents in the following form: . development of good information sources and easy access for everyone . high level of automation and robotization that release people from routine activities (intellectual types of activities as well) . increasing the role of information in the value of the product. According to the majority, relevant theoretical viewpoints are based on the “Post-industrial society” concept of the prominent American sociologist Daniel Bellin (1919-2011) [5]. The author puts forward that development of knowledge and technology has an influence on the progress of the society in this concept. He supposes that, in the post-industrial society, information-based intellectual technologies begin to replace machine technologies, the major political problem of this society is related to form, and characteristic of the government support towards science, not muscle strength and energy, but the information plays main role here. Followings are the main statements of the “Post-industrial society” concept:  economic sphere: transition from production of industrial goods to production of services;  employment sector: dominance of professionals and technicians class;  major direction: leading role of theoretical knowledges as the source of innovations;  future directions: control on technologies and technological values;  process of making decisions: establishing new intellectual technologies. In the D.Bellin’s writings of the 70’s. the idea of information society was not openly put forth, however, he considered the systematization of theoretical knowledge as the base principle of a new social order. D.Bell began to more profoundly influence the development of ICT society in the 80s, and since then, he became the supporter of information society concept. He began to consider this concept as the new phase of development of the post-industrial society concept [6]. Some scientists, however, consider that the idea of information society was firstly put forward by the prominent American scientist, founder of cybernetics Norbert Wiener (1894- 1964). He wrote in the 50s that the exchange of information between machine and human, human and machine, and machine and machine would play a crucial role in the society in the future [7-8]. One of the authors of information society concept, Japanese scientist Yoneji Masuda (1905-1995) describes the bases of information society as following: . new society will be based on computer technologies intended to replace or strengthen human mental activity;

4 www.jpis.az

Problems of information society, 2016, №2, 3-17

. being leading sector of intellectual production economy which is created and spread by means of new telecommunication technologies, information revolution will become a new production force of the society; . the mass production of cognitive and systematized information, technologies and knowledge will be possible; . the possibilities of cooperation and jointly problem solving will increase; . intellectual production will become leading sector of the economy; . “free society” will be the subject of the social activeness in the information society; . major target of the new society will be the realization of “value of time”. American scientist Alvin Toffler (1928) made a significant contribution to the development of the information society concept. His trilogy of books titled “Future Shock”, “The Third Wave” and “Powershift” were dedicated to this concept. “Future Shock” describes the influence of changes on people and institutions. In “The Third Wave”, the directions of changes that affect society are analysed. The “Powershift” is dedicated to the challenges of governance during the change period [9-11]. A.Toffler defines economy in the information society as following: - the role of exchange of information and knowledge increases; - production of diverse ranged, but not mass goods, due to the new information technologies; - the importance of traditional factors of production - labor, land, raw material and investment decreases; - electronic information becomes the major exchange tool instead of traditional money; - goods and services will become components of systems, which require the increase of volume of its standards. As a result, there will be struggle for the information containing those standards; - sever bureaucratic workplaces will be replaced by flexible, temporary work alliances united around a single project; - the number of diversity of forms of organization will increase, the exchange of information between them will become complicated; - narrower specialization process will deepen in specialty fields, it will become difficult for workers with unique knowledges to replace each other; - professionals. who could manage to unite imagination and knowledges with practical activities. will become the face of labor market; - as a result of waste in one manufacturing process becoming raw materials for other manufacturing process, there will be abundance in the society, and this will be possible through computerization of relevant monitoring processes and deepening of scientific and ecological knowledge; - manufacturers and consumers, separated as a result of industrial revolution, will reunite in the abundance producing cycle; consumer, besides money, will possess the market or economy related information that is significant for the effective production process. Eventually, the consumer will be able to speed up the production process. - The process of production of abundance will be possible both on local and global scale. This process will be possible as a result of going beyond the national borders, with joint efforts of many states. A.Toffler considers the time as one of the major values of the new civilization. According to him, rapid information flows and globalization lead to formation of “real-time economy”. A.Toffler mentions individualization of production and people’s preference to individualization in their activities, a sharp increase in the information exchange, preference to the self-governance in the political systems as main indicators of information society.

www.jpis.az 5 Problems of information society, 2016, №2, 3-17

American scientist Manuel Castells (1942) doesn’t mention the concept of “information society” in his writings. According to him, all societies used the information; therefore, all of them are information societies in fact. He names the new society as “network society” and defines age of information as an age of globalization. According to his views, collection, analysis and transmission of essential information are fundamental sources of productivity and power. He considers that the development of the ‘informationalism’ leads to the creation of network society and new economy. M.Castells writes in his book “The Information Age: Economy, Society and Culture” that the network connecting people, organizations and states will play a key role in the information age. M.Castells defines the international division of labor in a new society as followings: - producers of high value goods (information-based labor); - producers of large-volume goods (not based high labor productivity); - producers of raw materials (those with natural resources); - producers of abundant goods (those using provided labor resources). M. Castells shows the following characteristics of network enterprises: scope, interactivity, flexible governance, branding, consumer orientation. At the same time, he mentions that the transition of society into information age is not limited to technology and economy: “It also covers morality, cultures, ideas, as well as institutional and political structure of the society. It means the complete transformation of human life”. M.Castell’s the “Network society” concept arises from the recognition of information as the basis of the social structure. Thus, the author presents social structures as network structures that he accepts as new social structures. According to this views, operativeness, mobility and flexibility covering all areas of human life makes necessary the transition to network form of social structure: network system in the economy, interactive political system, single global information network - the Internet. M.Castells defines the problems of network (information) society as following: - problems associated with the internet management; - restrictions on the use of the potential of the internet, digital inequality; - problems associated with the development of information usage capability; - problems associated with the transformation of labor relations; - problems associated with an increase in the intensity of exploitation of natural resources; - anxiety of losing control over man-made technologies; French sociologist Alain Touraine (1925) calls information society as “programmed society”. He mentions the changes in the production sector, government and management relations, as distinctive features of this society. He considers that trade in the agricultural society, production in the industrial society, communication in the information society are the main business sector [13]. American scientist Peter Drucker (1909-2005) contribute to the information society concept in his book titled “Post-capitalism society” published in 1995 [14]. In this book, he displayes own views on the current situation and development perspectives of the Western civilization. He characterizes the transition from industrial agriculture to the knowledge and information-based economy as prevalence of private capitalist ownership, formation of system of new values of modern human and transformation of idea of national statehood into the idea of global society due to the influence of globalization of economy and society. According to P.Drucker, traditional factors of production - land, labor and investment are not eliminated, simply passed into the background. In his opinion, traditional resources of production (land, labor and investment) can be easily obtained by possessing necessary knowledge. He states that power and control are gradually passed from the investment owners to the owners of information and knowledge, using technologies of those resources.

6 www.jpis.az

Problems of information society, 2016, №2, 3-17

American economist Fritz Machlup (1902-1983) in 1962, for the first in USA economy, differentiated “production and expansion of knowledge” sphere, and introduced the term “economy of knowledge” to the scientific circulation [15]. According to German sociologist Tom Stoner (1927-1999), information society is the age of abundance; everybody will become aristocratic and philosopher [16]. According to him, since the food concerns of the people ended up at the end of industrial society, the end of the communicative society will eliminate the concerns about the financial needs. “As industrial society eliminated slavery, famine and epidemics, information society will wipe out authoritarianism, war and antagonism. The first time in the history, our speed of problem solving will exceed the speed of their creation”. T.Stoner considers that information can be accumulated like a capital and kept for future use. According to his views, national information resources will become the greatest potential wealth in the information society and therefore, first of all, attention should be paid to the information economy. Russian scientist Vladislav Inozemtsev (1968) considers that information technologies and resources lead us to “post-economic society” and here, creative activity, which is self-realization of personality, will replace the labor activity motivated from the economic [ers[ectives [17]. American politician Zbigniew Brzezinski (1928) puts forward “technetronic society” concept [1]. In his book titled “Between Two Ages: America's Role in the Technetronic Era”, he discusses that information society will turn into technetronic society. He considers that technetronic society is the formation of new cultural, psychological, social and economical relations as a result of developments in technology and electronics, particularly in computer and communication sectors. Canadian scientist Marshall McLuhan (1911-1979) is considered as one of the classics in the sphere of the mass communications theory. He considers the information technologies as the key factor affecting formation of socio-economic basis of new society. M.McLuhan states that electricity turns the earth into the global village through telecommunications, mass media and computers. According to him, mass communications become the part of the modern society and at the same time, have special power over it. American sociologist Herbert Schiller (1919-2000) deems that the interests of corporate capitalism dominate in the information sphere. According to his viewpoints, the development of information processes and technologies, first of all, serves for the interest of private business, not of the whole society. H.Schiller claims that there are, above all, traces of cooperative investment on the information technologies, all other interests are on the background. The views of another Russian scientist Nikita Moiseyev (1917-2000) on information society are of great importance. In his opinion, without providing free access to information, it is impossible to talk about information society - collective intelligence building globally. He considers that the second important factor for collective intelligence building is the people’s intention to share their knowledge. N.Moiseyev put forward a “collective wisdom” concept. He understands the “collective wisdom” as a system connecting people with information links. According to his views, the collective wisdom is the special information system which not only gathers and passes information, but also analyses and draws conclusion and unifies them in a special form. N.Moiseyev considers that, when the collective wisdom will have the features to impalement such functions, the mankind will take a step to the information society. English sociologist Anthony Giddens (1938) doesn’t accept the “information society” concept. He considers that the society has always been informed from the very begining, not in the modern era. In his opinion, the increase in the value of information in modern era doesn’t mean formation of a new society. According to the A.Gidden’s theory, people provide their financial and social life on the basis of abstract knowledge acquired from the mass media and educational institutions. People choose their lifestyle and model of behavior in the circumstance

www.jpis.az 7 Problems of information society, 2016, №2, 3-17 of having access to any information. However, in the developed societies, choices are not dependent on territorial, traditional and other factors [1]. Mark Poster (1941), a prominent representative of postmodernism, attaches great importance to the role of the Internet in the information society [1]. He considers that globalization process occurring under the influence of the Internet breaks down the nation states, the Internet gives individuals more freedoms than the citizenship, the Internet saves the citizen from tyranny of nation state and transforms to a free person. Such notions of M.Poster are considered threatening towards national interests, as these notions contain calls for civil disobedience. Italian scientist Gianni Vattimo (1936) studies information society from the aspect of cultural studies[21]. He considers the increase in the number and scope of the mass media as an important factor in the modern society. He notes that rapid increase in the number of broadcasting and cable television destroys the people’s trust for the realities of new world. The mass media brings alternative realities to society, increases the present realities and people become forced to live amid different cultures. German scientist Jurgen Habermas (1929) had conducted research on information management and manipulative technologies sectors [22]. He put forward the “Public sphere (Information sphere)” concept. Although the author agrees that the importance of information in the society increased, he doesn’t accept the idea the idea of creation of information society. J.Habermas shows the following features of crisis in the public information sphere: - information converts into goods; - information waste is increasing; - public institutions (libraries, museums, galleries, etc.) become commercial; - persuasion is observed in politics and advertising instead of discussion. The major characteristics of information society are more broadly discussed in the papers of Canadian scientist Don Tapscott (1947) titled “Electronic-digital society” [23]: Knowledge orientation. Intellectual labor becomes the basis of building material values and creation of revenue. Knowledge becomes the component of the good. Knowledge-oriented technical devices begin to be widely used. Information-control system is transformed into knowledges system. Digital presentation form of objects. Documents turn into electron-digital form. The communication between people takes the form of units and zeros. The transition from analog techniques into electronic-digital techniques occurs (communication, data recording systems, copying). Virtual characteristic. Physical objects and organizations become virtual. Virtual shops, warehouses, offices and brigades emerge. Data virtualization occurs and "virtual reality systems" are formed. Molecular structure. Administrative-team hierarchy comes to an end. Individual workers and teams gain the opportunity of freedom of activity and creating goods. The components, which are multifunctional and stipulated for multiple uses, are created. Integration. Internetworking interaction. A new type of enterprise – is a network element. Independent module organizations, which make up single service and production network, arise. Building material values, trade, social life is based on commonly used global infrastructure. Elimination of mediators. As a result of removal of agents, brokering services, wholesale centers from the economic activity direct relations between manufacturers and consumers arise. Convergence. The convergence of major sectors of economy and organizational structures occurs. Innovative environment. Innovation becomes the driving force of economic activity and success. Human imagination act as the main source of values instead of traditional success factors like access to raw material, productivity, scale and the value of labor force.

8 www.jpis.az

Problems of information society, 2016, №2, 3-17

Transformation of “manufacturer-consumer” relations. The borders between manufacturers and consumers wipe out. Knowledge and information of consumers are used in the production of goods, as well as in the use of information systems and program productions. Consumer could watch the creation of the desired product and make changes to it. Dynamism. New society acts in the real time. Trade becomes electronic, communication is momentarily realized, and direct control and management provide the real picture of the process with all of its necessary parameters. Global scale. Knowledges are beyond borders. The economy is becoming globalized. Building interaction and cooperation is not limited to time and place. Work can be carried out from everywhere, from home as well. Interdependence of states is increasing. The existence of contradictions. Mass social conflict is arising between working employees and workers with needless knowledge, dismissed workers, educated and non-educated, the ones with access to information highway and the ones without. International organizations dealing with multidisciplinary problems of information society. The necessity of deep understanding of the most radical changes in the history of the civilization occurred as a result of rapid development of ICT and its influence on all spheres of human activity and making future-oriented decisions in the new situation has led to the formation of a number of influential international and regional organizations in this sector. Approaches of those organizations on the solving of problems of information society are of multidisciplinary (complex view) character. The reason for putting forward the idea of holding World Summit on the Information Society, WSIS in 2003 (Geneva) and in 2005 (Tunis) stemmed namely from this necessity. [24,25]. The “Declaration of Principles” adopted at the Geneva Summit defines various conceptual notions on information society and common direction of political efforts of different states. The document also contains a set of philosophical, social, political, culturological and technological notions on information society. In the “Tunis Commitment”, adopted at the Tunis summit, it is declared that building information society is based on human interests, principles of the Charter of the United Nations, the Universal Declaration of Human Rights, and it is reaffirmed that freedom of speech and information, ideas and knowledge are of great importance for the information society. The key role of the use of ICT in enterprises in the economic development is also noted, and the necessity of struggle against emerging threats as a result of the use of ICT is emphasized. At the same time, the document takes the commitment the implementation of relevant monitoring and evaluation in order to define the level of development of the information society and to eliminate the digital differences. "Tunis Agenda for Information Society" contains the issues regarding identifying financial mechanisms to overcome the digital differences and increasing the focus on the Internet regulation issues. In 2014, “WSIS+10”Summit was held in Geneva for analysis of the activity on implementation of the decisions of the World Summit on the Information Society held in two phases in 2003-2005 [26]. The concept of action for the post-2015 period of "WSIS + 10" was approved at the Summit. At the concept, the participating countries are recommended to strengthen the activity in the following directions: - role of government bodies and all other interested parties in order to use ICT for development; - information and communication infrastructure; - information and knowledge accessibility; - creation of potential; - strengthening reliance and security in the use of ICT;

www.jpis.az 9 Problems of information society, 2016, №2, 3-17

- effective environment - ICT applications: benefits in all aspects of life (e-government, e-business; e- education, e-health, e-employment, e-ecology, e-agriculture, e-science); - cultural diversity and cultural identity, linguistic diversity and local content; - mass media - ethical aspects of the information society - international and regional cooperation. The United Nations Development Programme (UNDP) operates in the direction of creating favorable conditions for the development of ICT in developing countries [27]. The agency finances local, regional and international programs on the development of ICT by means of a special fund; Deals with the defence of the international law of organizations operating in the ICT sector; Makes efforts for expansion of international relations in the ICT sector; Makes recommendations and strategies for the implementation of programs on ICT. UNESCO, the United Nations’ agency specialized in the scientific, educational and cultural issues, to deliver its recommendations on challenges of the development of the information society [25, 28]. The agency acts with knowledge society concept instead of the information society concept. UNESCO considers that, for building fair information society, first of all, efforts should be made to explore and to solve the following problems: 1. ICT and gender issues; 2. Cultural and linguistic diversity; 3. Freedom of expression and the press 4. The problems of ICT and disabled persons 5. The problems of information ethics and accessibility of knowledge and information for everybody. UN’s other specialized agency - the International Telecommunications Union (ITU), deals with the following problems related to the formation of the infrastructure of the information society [29]:  equal access to ICT;  building broadband Internet network in order to solve socio-economic development issues;  use of ICT to eliminate the consequences of the climate change;  ensuring the security in cyberspace, including cybercrime and spam spread issues;  building reliance associated with the use of ICT;  elimination of information inequality;  use of ICT to ensure the safety of life;  creation of new generation networks. Research works of ITU are carried out within activity of three sectors: the standardization sector, telecommunications sector and the development sector. Each sector has several research groups involved in the study of the specific issues related to the respective problem. In general, studies carried out with the support of ITU mainly have the application nature and focused principally on the development of infrastructure. United Nations Conference on Trade and Development (UNCTAD), within its mandate, publishes a series of articles on different aspects the use of ICT [30]. Organization for Economic Cooperation and Development (OECD), within the framework of its activities, deals with the use of ICT for the development purpose [31]. Recently, the organization carried out studies in the following topics: information security (inviolability of private life, digital identity problems, the spread of spam); stimulation of innovations; information society infrastructure (critical infrastructure, broadband technologies, new

10 www.jpis.az

Problems of information society, 2016, №2, 3-17 generation technologies); "clean" technologies and "smart" software; digital content (including piracy issues); increase of accessibility of the official information and effective usage of it; information economy (Internet economy, the competition in the telecommunications sector, consumer protection, support and training); e-government (user-oriented services); e-commerce; information industry (including employment in the ICT sector); mass media. The World Information Technology and Services Alliance (WITSA) incorporates 46 national associations in the ICT sector [32]. Alliance members make up 90% of the global ICT market. The main mission of this organization includes strengthening the international competition in the ICT sector, protecting intellectual property rights, unifying the efforts of states in order to ensure the information security, eliminating the differences in the level of education and qualifications on ICT between countries, ensuring the sustainable development of the Internet and e-commerce. In 2010, "Digital Diary for Europe” was adopted by the European Commission [33]. The document has a special section devoted to researches and innovations. European Commission prepared the development strategy until 2020 for relevant researches and innovations in order to strengthen the innovative impact of ICT as goods and services on the european markets. The goals and objectives of the strategy focus on improving the living standards and meeting social needs. The main statement of the strategy are related to normative legal (certification, standardization, etc.) and organizational (public-private sector partnerships for building "road maps" on technological sector, establishment of knowledge transfer, as well as ensuring open access to scientific information and publications created at the expense of budget funds etc.) issues. Influential scientific research centers dealing with multidisciplinary problems of the information society Multidisciplinary scientific problems of the information society are one of the priority areas of the world science. In many countries of the world, there are special scientific centers conducting research in this direction. Some of these centers possess international or regional status. In other words, scientific projects covering different countries are carried out and research scientists and experts from different countries are involved in these centers. Employees of one of such centers European Network for Information Society Research, ENIR, are doing important work in the Information Society sector [34]. The Network incorporates more than 100 organizations from European countries, as well as Brazil, China, Mexico, the United States, Tunisia, Switzerland and Japan. The major users of the network include the Commission of the European Communities, national and regional administrative bodies, various business associations. Among the main topics of research in the areas of social importance - health, business, public administration, employment, ensuring the active participation of citizens in public life, the disabled and the elderly in the service sector, education - application of ICT have a special significance. Studies of participants cover areas such as use of indicators, market research, citizen-orientation provision of online public services, employment model for older people, the impact of ICT in various application spheres, assessment of strategic and program documents and search and analysis of advanced practices. European Cooperation in Science and Technology, COST, also conducts research in the field of development and application of ICT [35]. This organization coordinates the activities funded by the states in several European countries as an intergovernmental structure. Also the organization ensures international cooperation of national scientific research institutes, universities and business organizations of Europe. COST's main research directions include:  biomedicine and molecular biology;  food and agriculture;  forestry (products and services);

www.jpis.az 11 Problems of information society, 2016, №2, 3-17

 materials, physics and nanotechnologies;  chemistry, molecular research and technologies;  management of earth and environmental sciences;  information and communication technologies, transport and urban development;  citizens, society, culture and health. European Research Coordination Agency, EUREKA, currently covers 39 countries [36]. The objective of the establishment of EUREKA is to eliminate lag of Europe from the US and Japan in scientific and technical spheres. This network contributes to the development of international market oriented research and innovations by supporting small, medium and large businesses, universities and research institutions. The network participants are united in clusters on long-term basis and principles of "government-business partnership". New products and services which promote the economic growth and social development of Europe are taken to the market through EUREKA network. Results of the relevant research projects have been used to create devices and technologies for monitoring of GSM technologies, mobile communication, navigation systems, smart cards supporting mobile and e-commerce, the software of the film industry, environmental pollution. A research institution in Europe called Future of Identity in the Information Society, FIDIS, - the network of academic institutions and organizations which is specialized in study of identity problem in the information society [37]. This network studies problems of submission of personal data in electronic form, including various identification methods, anonymity and pseudonimity problems, the use of numerous identities and its goals, profiling, high-tech IDs, compliance of identifiers, inviolability of private life and the consequences of its violation etc. Information Society Research Group’s, School of Information & Library Studies, University College Dublin activity is also very efficient [38]. The research group's interests include the spheres as information policy, e-government, the impact of technology and the Internet on public relations and cultural models, social investment of the information society, philosophical aspects of the information and communication, ethnographic aspects of the activities of organizations and information systems, information requirements of societies. Yale University law school, the United States, is engaged in the study of the problems of the information society. The relevant research project of the school is called: "The Information Society Project: Memes (meme – is a cultural information unit), Genes and Bits at the Yale Law School" [39]. Within the project, the studies on acquiring knowledge, online civil freedoms, e- education, reforms in the field of intellectual property and innovations etc. are supported, the lessons are held, ideas are formed and spread. Alexander von Humboldt Institute for Internet and Society (HIIG) at the University of Berlin is studies the dynamic relationship between the Internet and society [40]. The aim is to study the interaction of the socio-cultural, legal, economic and technical norms. The Institute carries out joint research projects at national and international levels. HIIG puts forward new ideas and suggestions related to the opportunities and challenges of the information society through fundamental and empirical studies. Berkman Center for Internet & Society (BCIS), is research institute operating under the Harvard University [41]. The main mission of the law-focused center includes the study of the problems related to characteristics of cyberspace - trends, dynamics, norms and standards. Issues of inviolability of private life, protection of intellectual property, management of content and e- commerce are also studied here. Research projects as "Digital media law", "Internet and democracy" were carried out at the center. The Center's Internet portal offer opportunities for teachers and students, entrepreneurs, lawyers, programmers to attend the lectures and hold free discussions. BCIS also sponsors scientific conferences and other meetings. Canadian Internet Policy and Public Interest Clinic (CIPPIC), operates as a Legal Clinic at the University of Ottawa [42]. The main objective of the research center is to give scientific

12 www.jpis.az

Problems of information society, 2016, №2, 3-17 support for the country to fair and balanced policy-making process on technologies. In other words, the center holds a mission to represent the interests of the society on different issues that arise at the intersection of technologies and legal system in the country. CIPPIC, on a regular basis, is represented as expert group at the Canadian parliamentary committees, as well as various courts. In addition, the organization is actively involved in the activities of the Internet Governance Forum. The research center offers consulting services to individuals and legal entities, carries out public awareness campaigns on various legal issues, as well as is engaged in educational activities. The Centre for Internet and Society (CIS), operating in India, is a non-profit organization doing multidisciplinary researches on the Internet and other digital technologies [43]. Directions of activity of the center cover the areas such as accessibility of digital knowledge for citizens, protection of intellectual property rights, Internet regulation, telecommunications reforms digital privacy and cyber security. CIS, by means of the Internet and digital media technologies, also pays special attention to the study of social and cultural processes, transformations taking place in the society. Center for Internet and Society (CIS) at the Stanford Law School, consists of academics, legislators, students, programmers and security experts [44]. The researches on the legal aspects of technology, including ensuring freedom of speech, inviolability of private life and application of innovation are carried out at the center. Law students and the general public are provided with the analysis of political issues arising at the intersection of law, technology and public interests, as well as with relevant educational materials here. The center also finances a number of public events, as well as scientific conferences and seminars. Nexa (means "related things" in Latin language) Center for Internet and Society of the Polytechnic University of Turin (Italy) is established at the management and computer engineering department [45]. Operating as a scientific research institution, center studies the information society and the Internet on the basis of multidisciplinary approach - technological, legal and economic aspects. The research center jointly works with the European Commission, national and local authorities, businesses and other organizations. The main objective of the center includes the study of the main factors that characterize the Internet, their dynamic and impact on society. Oxford Internet Institute (OII) is established at the University of Oxford in order to explore the social consequences of the Internet [46]. OII covers various scientific fields, including politology, sociology, geography, economics, physics and philosophy. A wide range of the methods and sources of information are used in the institute to study the Internet environment. Books and journals published by OII present research results on the topics such as inviolability and security of private life, e-government and e-democracy, virtual economy, information ethics, "smart city", online games, big data, Internet geography, digital restrictions, digital humanities, Wikipedia. Taiwan Institute for Information Industry (TIII) is established in Taiwan, in order to provide scientific support for formation of the information society, innovative application of information technologies, development of the knowledge economy [47]. The Institute also has the functions of scientific support and coordination between the Ministry of Economy and leading instituions on ICT for economic development of the country. The institution’s activity on Taiwan's achievement of global competitiveness ability in ICT industry is highly appreciated. The departments of "smart" network systems, innovative applications and services, data analytics and cyber security, e-education operate within TIII. Information Society Center in Berkeley (ISCB) established in the USA, is engaged in the study of social impacts of the last information revolution [48]. The head of the research council of the Center is prominent researcher of the information society M.Castels. The main activity of the research center includes:

www.jpis.az 13 Problems of information society, 2016, №2, 3-17

. the problems of the global information society and their solution models; . use of ICT in the social sphere; . increase of equal social opportunities. Multidisciplinary researches on the problems of information security and confidentiality which became pressing issue due to the development of information technologies are conducted at the Center for Information, Society and Policy (CISPA) based in Chicago [49]. The center unites experience of computer science scientists, psychologists, lawyers, business experts and others. Korea Information Society Development Institute (KISDI) deals with the issues of application of ICT in the economy [50]. Being the leading think tank consisting of prominent experts from South Korea, KISDI puts efforts to achieve positive changes, opportunities and achievements in the life of the country. This institute plays an important role in turning in South Korea becoming a leading country in ICT industry in the world. The institute focused its research efforts on the future development of the information society, informatization, creative economy, telecommunications sector and electronic media of the country. Prestigious scientific journals dedicated to the multidisciplinary problems of the information society Currently, a range of prestigious journals, dedicated to the multidisciplinary problems of the information society, are published in different countries of the world. The results of theoretical and empirical researches on various aspects of the information society are published in such journals. The first such journal was established in 1981 in the United States. In the journal titled "The Information Society" the articles on discussions about understanding and analysis of the impact of ICT in the life of society, conceptual and strategic documents, methodological approaches, different trends (social transformations, changes in cultural features, etc.) related to the relevant fields are published [51]. One of the Springer's publications, the "Universal Access in the Information Society" journal, publishes the results of scientific-research works on designing, development, working, use and impacts of information society technologies, as well as standardization, policy and other non-technological issues facilitating and expanding common availability. The results of theoretical, methodological and empirical studies are preferred in the journal [52]. Another journal published in the Springer - "AI & Society" is also multidisciplinary. The journal publishes research results of management, security, cultural, social, economic, ethical, philosophical problems of the application of new technologies [53]. Russian-published "Информационное общество" (Information Society) journal is a periodical scientific publication specializing in complex problems of the development of the information society [54]. 40% of the articles published in this journal are dedicated to e- government, 20% to e-education, 8% to e-health, 8 % to the use of the Internet. "Journal of Law and Policy for the Information Society" published in the UK is a periodic publication specialized in multidisciplinary problems of the information society [55]. The journal includes the headings such as intellectual property protection, e-democracy, cybersecurity, inviolability of private life. British "Journal of Information, Communication and Ethics in Society" is a periodic publication presenting the results of researches on multidisciplinary problems related to the information society [56]. The magazine introduces the articles on the study of social and ethical issues such as planning, development, implementation and use of new mass media and information and communication technologies. The audience of the publication consists of scientists from anthropology, business, computer science, journalism, philosophy, political science, psychology and sociology spheres.

14 www.jpis.az

Problems of information society, 2016, №2, 3-17

India's scientific publication titled "International Journal of Information Library and Society" publishes articles on the role of libraries in the information society, ensuring citizens' rights to information, pressing problems of e-libraries [57]. The scope of the topics covered by the periodic publication is very broad: data protection and copyright, e-libraries, formalizing e- documents, e-resources, new technologies, library and the society, library 2.0., scientific libraries, e-library management etc. Conclusion Despite the certain contradiction in the approaches provided by the authors of respective scientific-theoretical concepts, the researchers unanimously agree on one issue that information society has period of radical qualitative changes, which serve for the rapid development of the humanity. All the relevant studies are focused on the perception of the system of values and new relationships established at this period, and leaned towards the exploration of ways for solving the problems. The efforts and calls of international organizations serve to support the establishment of the ideology of information society, importance of which is scientifically approved in the world, from organizational and methodological aspects. Analysis of the well-known scientific-theoretical concepts, pleas of international organizations, activities of leading scientific research centers and journals show that solving the problems of this sector requires a complex approach, multidisciplinary scientific methodology. Because, the information society is a virtual alternative to the real world. The process of virtualization covers all areas interacting with one another. Based on the respective analysis, scientific-theoretical problems of the formation of the information society can be grouped as follows: Technological: advantages of the use of ICT in all spheres of human activity and its innovations; the formation of the infrastructure of the information society (critical infrastructure, broadband Internet development, the creation of next-generation networks, etc.); "Smart", "green" and other new technologies, stimulation of the development of data analytics, ensuring information security, the use of valid identification methods; Legal: legal aspects of ICT; ICT standardization and certification issues; ensuring the rights of citizens to information; the legal regulation of the Internet; fight against cybercrime; intellectual property and protection of consumer rights; Economic: the transformation of traditional economic relations; the globalization of the economy; the increasing role of information factor in the production and economy; the formation of information and knowledge economy; stimulation of innovativeness and creativity in the new economy; the development of e-commerce; changes in the nature of labor, the creation of new forms of trade; Political: new public management concepts; concepts and models of building the information society; information policy; the concept of e-government; public sector-private sector-civil society cooperation in the information society; digital democracy; the role of freedom of speech in ensuring human rights and political processes; the manipulation of information; international cooperation in the field of information society; Social: the formation of a new system of values of human and society; the structure, features, development methods and philosophical understanding of the new society; processes which act as an important stimulator of changes in the quality of life; virtual reality features; social networks and social media; digital divide; the importance of social investment; to ensure the interests of marginal groups in the information society; Locational: globalization processes occurring as a result of the spread of information and communication networks and its positive and negative aspects; Psychological: changes in social and individual consciousness, feelings, and moods with the impact of ICT, addiction to information;

www.jpis.az 15 Problems of information society, 2016, №2, 3-17

Culturological: culturological value of information; the increase in informativeness of modern cultural sphere; the expansion of the role an influence of the mass media in modern society; information culture; protection of cultural and linguistic diversity. References 1. Webster F. Theories of the Information Society, Routledge, 2007, 312 p. 2. Manakova I.Y. Methodological aspects of the study of post-industrial society // Bulletin of Voronezh State University. Series: Philosophy, 2011, No1, pp. 74-87. 3. Kumar K. From Post-Industrial to Post-Modern Society: New Theories of the Contemporary World, John Wiley & Sons, 2009, 304 p. 4. Bell D. The Coming of Post-industrial Society: A Venture in Social Forecasting, Basic Books, 1976, 507 p. 5. Bell D. Social framework of the information society // New technocratic wave in the West, Moscow, Progress, 1986, pp. 330-342. 6. Litvak N.V. About the classification of the information society // Sociological researches, 2010, No8 pp. 3-12. 7. Wiener I. Cybernetics, or Control and Communication in the Animals and the Machines, Moscow, Soviet Radio, 1968, 328 p. 8. Masuda Y. The Information Society as Post-industrial Society, Washington, 1981, 286 p. 9. Toffler A., Toffler Н. Creating a New Civilization: The Politics of The Third Wave, Atlanta, Turner Publishing, 1995, 112 p. 10. Toffler E. Future Shock, AST, 2008, 558 p. 11. Toffler E. Metamorphoses of power: knowledge, wealth and power on the threshold of the XXI century, AST, 2009, 668 p. 12. Castells M. The Rise of The Network Society: The Information Age: Economy, Society and Culture, Wiley, 2000, 624 c. 13. Clark J., Diani M. Alain Touraine, Psychology Press, 1996, 381 p. 14. Drucker P. Post-Capitalist Society, Routledge, 2012, 212 p. 15. Machlup F. Production and dissemination of knowledge in the United States, Moscow, Progress, 1966, 462 p. 16. Stonier T. The Wealth of Information: A Profile of the Post-industrial Economy, Thames Methuen, 1983, 224 p. 17. Gordon W. T. Marshall McLuhan - Escape Into Understanding: A Biography, Gingko Press, Incorporated, 2004, 465 p. 18. Inozemtsev V.L. Beyond the economic society: postindustrial. Theory and postecon. trends in the modern world, M., Academia: Science, 1998, 639 p. 19. Salvaggio J.L. The Information Society: Economic, Social, and Structural Issues, Routledge, 2013, 152 p. 20. Moisev N.N. The fate of civilization.The path of mind, М. 2000, 224 p. 21. Vattimo G. Transparent Society, M., Logos, 2002, 128 p. 22. Habermas J. The Structural Transformation of the Public Sphere: An Inquiry Into a Category of Bourgeois Society, MIT Press, 1991, 301 p. 23. Tapscott D. Electronic digital society, M., Refl-book, 1999, 432 p. 24. http://www.publicadministration.un.org/wsis10 25. Chernov A.A . The formation of a global information: problems and prospects, M., "Dashkov and K", 2003, 232 p. 26. ITU, Outcome WSIS+10, Ceneva, 2014, 50 p. 27. http://www.sdnp.undp.org/it4dev 28. http://en.unesco.org/themes/building-knowledge-societies 29. http://www.itu.int/en/Pages/default.aspx

16 www.jpis.az

Problems of information society, 2016, №2, 3-17

30. http://unctad.org/en/Pages/Home.aspx 31. http://www.oecd.org 32. www.witsa.org 33. http://ec.europa.eu/index_en.htm 34. http://www.enir.org 35. http://www.cost.eu 36. http://www.eurekanetwork.org 37. http://www.fidis.net 38. http://www.ucd.ie/ics/research/informationandsociety 39. https://www.law.yale.edu 40. http://www.hiig.de/en 41. https://cyber.law.harvard.edu 42. https://cippic.ca 43. http://cis-india.org 44. http://cyberlaw.stanford.edu 45. https://nexa.polito.it/noc 46. http://www.oii.ox.ac.uk 47. http://web.iii.org.tw 48. http://www1.icsi.berkeley.edu/BCIS 49. https://www.kentlaw.iit.edu/institutes-centers/center-for-information-society-and-policy 50. http://kisdi.re.kr/kisdi/jsp/fp/eng/about/KE_31000.jsp 51. http://www.indiana.edu/~tisj/index.htm 52. http://www.springer.com/computer/hci/journal/10209 53. http://www.springer.com/computer/ai/journal/146 54. http://www.infosoc.iis.ru 55. http://www.is-journal.org 56. http://www.emeraldinsight.com/journal/jices 57. http://www.publishingindia.com/ijils

www.jpis.az 17 Problems of information society, 2016, №2, 18-23

Yadigar N. Imamverdiyev Institute of Information Technology of ANAS, Baku, Azerbaijan [email protected] SOCIAL MEDIA AND SECURITY CONCERNS Social media is not only a convenient platform of communication and file sharing, but also a socio-political impact and management tool, and an arena of confrontation and information warfare. Depending on the purposes of its users, social media can create a range of national security threats. The article describes a number of risk scenarios of the social media that can pose a threat to national security, and analyzes the methods of creating bots - the fake actors and managing them, and the experience of certain countries in this area. The article provides information on the available online services that can be used for social media monitoring and analysis. Key words: social media, national security, social media monitoring, social media analytics, information influence, information warfare.

Introduction

Social network services created on the basis of Web 2.0 ideology in a very short period of time, in the last decade, turned into a social media generating and distributing unique content from a convenient tool for communication and file sharing [1]. Today, social media includes blogs (Blogger, LiveJournal), micro-blogs (Twitter, FMyLife), social networking services (Facebook, LinkedIn), wiki-s (Wikipedia, Wetpaint), social bookmarking (Delicious, CiteULike), social news (Digg, Mixx), reviews (ePinions, Yelp), multimedia sharing (Flickr, Youtube). Facebook, Twitter, YouTube and other social media services have become an integral part of the online life of the majority of Internet users. Social media has a number of advantages in comparison with traditional media: social media is available; requires minimal expense; is open to everyone - any person can connect to the global communication platform and can act as a source of information; is more dynamic and flexible; feedback opportunities are extensive - the audience has the opportunity to interact with a source of information; offers high degree of individualization; unites people in a single platform and allows to exchange large amounts of information. Social media users have a quite diverse spectrum. Conventional users benefit from social media as a means of communication, acquaintance, sharing daily data and images. The affordable feedback opportunities of social media makes it an effective communication and impact channel. Recently, public authorities, political parties, civil society and private sector is trying to widely use the potential of social media [2,3]. Social media brings some threats as well. The threats can be directed against the individuals and social groups, in general, against the state and society. Detailed information on the dangers of social networks, directed against individuals, is provided in [4], and specific recommendations are presented. In recent years, the representatives of the state authorities in all countries of the world have repeatedly stated concerns regarding the threats that social media can pose to national security [5.6]. There are various risk scenarios - wide usage of social media by terrorists, external forces using social media as a mean to influence internal politics of the country and so on. There is also a practical basis for these concerns, and "Arab spring" has proved that social media is a strong weapon that can be used to divert the masses, dramatize events, and realize social changes and revolutions [7]. The main objectives of the information policy of the state authorities include to inform the citizens about their activities and to organize feedback with citizens by the means of mass communication. At the same time, government agencies are obliged to respond to information threats that can create conflict and social tension, can lead to the wrong public opinion and damage the reputation of the state authorities.

18 www.jpis.az

Problems of information society, 2016, №2, 18-23

The article describes a number of risk scenarios of the social media that can pose a threat to national security, and analyzes the methods of creating bots - the fake actors and managing them, and the experience of certain countries in this area. The article provides information on the available online services that can be used for social media monitoring and analysis.

Social media: risk scenarios

The main concern of some countries is related to the usage of social media by terrorist, extremist and radical groups. Almost 90% of the organized terrorism on the Internet is carried out through social media sites. Social media provides an opportunity for terrorists to address millions of people in a few seconds. Terrorist groups spread their appeals through YouTube, Facebook, Twitter and other sites, and involve tens of thousands of people to their ranks. Sometimes, the countries find the way of solution in restricting access to certain social media sites. For example, officials of the European Commission demand more proactive regulation for the online participation of violent extremist groups in Google, Facebook, Twitter and other social media platforms. Officials propose to carefully consider the content posted by the users before uploading it, or to ban the entry of some groups to the social media platforms in general. Tweets, sensitive to religion, are becoming widespread. People are attacking each other's religious values through social media. The circulation of images insulting religious and racial feelings in social media creates tensions among the masses. Many politicians and public figures have pages on social networks. Social media is widely used as the means of political propaganda in election campaigns [8]. Some opposition politicians have earned their current status as a result of their active participation in social media. Blogs have become an important tool of political parties and public movements for highlighting actual events. Social media is also used as a convenient tool for organizing socio-political activities. Political parties, NGOs and hackers can pose a serious threat to political stability by using social media. Social media can be used as a tool for exporting revolution ("Facebook revolution", "Twitter revolution") to various countries. In 2009-2011, the political crises in Iran and several other Middle Eastern countries, the protests in Ukraine and Russia showed that social media can be used to mobilize protesters, and a small number of public opinion leaders is enough for it [9, 10]. It is known that intelligence agencies obtain a significant portion of the intelligence information (according to some confessions 80%) from open sources. Intelligence methodology based on open sources (Open Source Intelligence - OSINT) includes search, selection and collection of information from open sources, as well as its coordinated and cross analysis. The main information sources include media, public reports of public authorities and private companies, official press conferences, various official statements, materials of various conferences and seminars, and so on. Social media is also a great opportunity for intelligence agencies [11]. In 2011, during the operations of NATO forces in Libya, social media was widely used as a mean of transmission of intelligence data in real time. The relevant information was processed in a special operation center created in Napoli, Italy [12]. Social media has a great military potential – the usage of mobile phones with GPS function provides unlimited opportunities. It takes a few seconds to share geo-information data of an object with its photo in social networks. Social media was used to help the identification of surface targets during air attacks of NATO forces in Libya operations in 2011. The information obtained from social networks and other Internet sources allowed online observation of the progress of the military operations [12]. Thus, social media has become a powerful tool of propaganda, disinformation, conscious manipulation, and collection of individual, business and intelligence information [13]. About the information impact mechanisms of social media Although the social media phenomenon occurred in the last decade, its theoretical foundations were formed long before in the researches conducted in the world's foremost research

www.jpis.az 19 Problems of information society, 2016, №2, 18-23 centers on social psychology, media studies, social information science, social network analysis, complex networks, complex dynamics, complex systems, network wars etc. Social media reveals wide opportunities for experimental verification and real application of the results of these researches. The research centers include Los Alamos National Laboratory, where the world's first atomic bomb was prepared, Stanford University, Mitchell Social Network Analysis Center of Manchester University, the Santa Fe Institute, IBM, RAND Corporation and others. According to the views of the social network, humanity consists of a large number of societies, and each person is a member of a number of communities at the same time. The people who a person communicates with are, in their turn, also members of several communities. This chain of relationship creates a number of information channels covering all the mankind. Information can be spread through these channels at any scale as, according to the small world model, all the people are related with each other through common friends and usually the number of common acquaintances is not more than 6 [1]. Understanding the essence of network structures and distribution channels can lead to more effective results in terms of the impact on the audience. It is possible to influence the whole society, including decision-makers through the information published in media and accepted by the society in the right direction. The mechanism of the information impact on society through network structure is identical everywhere: a customer determines the task, its executors build a network structure consisting of public organizations, journalists (or the whole media and media-holding), political activists, members of an informal movement, in some cases criminal and extremist groups for carrying out this task. They involve some of them with grants, some with promises of political promotion and others simply with money. The involvement of people addicted to the idea given to them plays an important role. They are the driving force, they are able to convince people and to lead them to any radical activities. However, the peculiarity of the network warfare is that similar operations are not a sudden action, but there are more of them, they occur in different places at different times and carried out by different organizations. These actions jointly contribute to the overall result. The small actions are similar to bee’s nest – a bee needle is not dangerous, but the more bees are, the more they sting, and it makes people run. The experts of RAND Corporation call this principle "swarming" or "herd principle" [14,15]. "Herd" demonstrates itself as "micromovements", "stings", "reminders": media hype, discussions on various topics that are imposed on the society, various demonstrations, multitude of armed and unarmed physical confrontations. The pre-determined experts willingly express their desired opinions on the screen or on the Internet, the journalists publish scandalous articles, the human rights activists hold rallies and write open letters, and the lawyers give interviews about the arbitrary approach towards the persons who they defend and so on. Fake actors of social media "The Guardian" newspaper (UK) reported in 2011 that the US Department of Defense has created a special software to secretly manipulate the mood of social network members with the help of fake “online persons” [16]. Those fake persons had to intellectually influence the Internet users in order to contribute to the dissemination of American propaganda. Defense Advanced Research Projects Agency of the US Department of Defense (DARPA) in 2011 announced a tender for the creation of the software within the framework of Social Media in Strategic Communication (SMISC) program, for such operations [17, 18]. These works could be considered as continuation of the “Operation Earnest Voice”, OEV, carried out in Iraq in 2003. It is possible to create a network of fake social media actors, which is connected to information phase of different countries with the help of software, created by Ntrepid company within SMISC. As a result of this work, the impression of the presence of real people in different countries around the world, not "Sock Puppets”, is formed. Details of fake "autobiography", "characters" are created separately for each actor. The document about the online management service of identities says that each fake user profile, all the features of the character should be

20 www.jpis.az

Problems of information society, 2016, №2, 18-23 fully adequate from technical, cultural and geographical point of view. IP addresses for those fake profiles are camouflaged in a way that everything proves that fake actor has placed all the materials in the displayed area. As a result, even experienced opponents become incapable of detection of the facts on manipulation with these fake online persons, they can easily gain the trust of local bloggers. Management service of online people created by Ntrepid allows one operator to manage more than 10 fake social media accounts. Operations management center is McDill airbase (Tampa, Florida, USA), where the US Central Command headquarter (CENTCOM) is located. Open information on the establishment of similar structures in several countries can be found. For instance, the information about the plans for the creation of software and hardware complex for "monitoring and analysis of the military-political, socio-economic and socio-political situation in the country and the world" on the basis of data collected from various open sources for the Ministry of Defense of Russia was reported in the press. Federal Intelligence Service of Germany, in the next five years, plans to spend 300 million euros on the creation of technologies that enable real-time monitoring and analysis of social networks outside the country, as well as capturing and decoding network traffic. The program, main objective of which is the early detection of cybercrime, has been named as "Strategic Technologic Initiative" (Strategische Initiative Technik, SIT). A brief overview of the SMISC program Explanatory document for the tender of SMISC program states: "the conditions of the operations conducted by armed forces are rapidly changing under the influence of blogs, social networks, file exchange services (such as YouTube) and mobile technologies. The wide spread of social services can have a deep impact on the nature of the conflicts. The efficient use of these services will enable the armed forces to more efficiently conduct information support for operations ". The main objective of the SMISC program is the use of new social network science built on the basis of emerging technology. In a particular case, SMISC will create the automated and semi- automated tools and means to support the systematic use of social media on the datum scale by the operators and ensure the timely implementation of the four specific targets of the program: 1. To reveal the formation, develop and spread ideas and concepts; identify, classify, evaluate and follow purposeful or misleading (deceptive) information and disinformation. 2. To reveal structures and influence operations of persuasive campaigns on social media websites and communities. 3. To identify participants of the persuasive campaigns and their intentions, and to evaluate the effects of these campaigns. 4. To conduct opposite operations against the revealed operations of enemies. Technology sectors of SMISC are grouped according to the above stated four key targets: 1. linguistic tips, information flow templates, topic trend analysis, narrative structure analysis, mood detection and intellectual analysis models of opinions; 2. observing concepts and ideas in society, graph analysis / probability judgment, character detection; 3. stimulation of individuals, modeling of newly established communities, trust analysis, modeling of network dynamics; 4. automatic generation of content, social media bots, crowd sourcing. Studies show that traditional approaches with static graph models to social media modeling usually have wrong results. Therefore, it is necessary to take into account the dynamics of behaviors and SMISC is interested in the use of many tools to implement it [18].

Social media monitoring tools

The rapid increase in popularity of social media and rise of its economic and socio-political role requires the establishment of information systems for its monitoring and analysis [19]. However, there is a lack of data for social media monitoring and analysis systems for

www.jpis.az 21 Problems of information society, 2016, №2, 18-23 government bodies, as they were launched in 2010, and are not widespread yet [20]. Currently, most of the introduced social media monitoring tools in the market are focused on business issues. They identify how many times the names of certain brands, companies, products or services have been mentioned in social media, users’ attitudes towards them (positive, negative, neutral), detects the tone of opinions, divides the authors of opinions into segments according to gender, age, location, interests and etc. It should be mentioned that there are several monitoring tools in popular social network services. For instance, Facebook Insights, Google Analytics, Twitter Analytics, Analytics LinkedIn, Pinterest Analytics. Currently, web portals in English, such as SumAll, Sysomos, UberVU, SproutSocial, in Russian, such as YouScan, Brand Analutics, Babkee, BrandSpotter, Buzz Look, IQBuzz, SemanticForce, Wobot, Крибрум etc., offer social media monitoring services. Following paragraphs provide brief information on some of the monitoring systems. Seesmic (seesmic.com) - free service for social media monitoring. It supports Twitter, Facebook, LinkedIn, Chatter, Google Buzz, Ping.fm. Seesmic has application programs for the Internet, personal computers, iPhone, Android, Windows Mobile. In essence, Seesmic twitter – is client, was created using Adobe Air library, therefore could work on many platforms. Socialmention (socialmention.com) – is a free platform for the search and analysis of information on social networks. Socialmention searches meets on selective services or all social media that it supports. Besides, analysis of reminder and tones, related keywords, popular sources and etc. are included. Socialmention contains more than 100 social media sources including networks, social bookmarks, blogs, forums, social services. Hootsuite (hootsuite.com) – is multifunctional service working with social media. The emphasis of service was on Twitter. Hootsuite enables working with Facebook, LinkedIn, MySpace and Foursquare, and WordPress blogs and could connect to Ping.fm. Hootsuite has several opportunities to work with different analysis. For instance, it is possible to connect to Google Analytics. HootSuite works on a number of mobile platforms: iPhone, Android, Blackberry. Mobile applications are free. Twitalyzer (twitalyzer.com) – is analytical program-client for Twitter. and enables to monitor the number of links, to analyze the positive and negative comments, and to divide the audience into segments. Twitalyzer has been integrated with Google Analytics system and works with interactive diagrams and graphic tools. TweetDeck (tweetdeck.com) – is tool for the monitoring and management of information on social networks like Twitter, Facebook, MySpace, LinkedIn, supports various filters, including filtering based on keyword, works on different platforms. Despite existing and new services emerging each month, as well as presented opportunities, the whole social media monitoring systems are based on typical software [21-23].

Conclusion

Along with functions as communication, information, and exchange of views, recently, social media is widely used as information impact tool, and becomes the stage for information confrontation and information warfare. Monitoring and analysis of social media data, without violating human rights and freedom of expression, enable to take the pulse, set the mood and detect expectations of the society. It creates ground for effective dialogue for the government with citizens, private sector, political parties and movements, civil society institutions. Social media analysis enables to timely detect emerging threats and implement counter-measures. References

1. Alguliyev R. M., Imamverdiyev Y. N. Abdullayeva F. C., Social Networks. Baku, "Information Technology" Publishing House, 2010, p.287 2. Wright D. Hinson M., Examining how public relations practitioners actually are using social media // Public Relations Journal, 2009, vol. 3, no. 3, pp. 1-33.

22 www.jpis.az

Problems of information society, 2016, №2, 18-23

3. Under the general editorship by Alexeyeva E.G. Impact through social networks, M., Foundation "FOCUS-MEDIA", 2010, p.200 4. Hogben G. (ed.) Security issues and recommendations for online social networks. ENISA Position Paper No.1, October 2007, 33 p. 5. Montagnese A. Impact of social media on national security. Centro Militare di Studi Strategici: Research Paper 2011 STEPI - AE-U-3. 2012, 36 p. 6. Chen Y. Research on social media network and national security. W.Du (ed.) Informatics and Management Science II, Lecture Notes in Electrical Engineering, 2013, vol. 205, pp 593-599. 7. Khondker H.H. Role of the New Media in the Arab Spring // Globalizations, 2011, vol. 8, no.5, p.675-679. 8. Adamic L. A., Glance N. The political blogosphere and the 2004 US election: divided they blog / Proc. of the 3rd international workshop on Link discovery, 2005, pp. 36-43. 9. Elson S. B., Yeung D., Roshan P., Bohandy S. R., Nader A. Using social media to gauge Iranian public opinion and mood after the 2009 election. RAND Corporation Technical Report. 2012, 110 p. 10. Tan Z., Li X., Mao W. Agent-based modeling of netizen groups in Chinese Internet Events // Quarterly SCS M&S Magazine, 2012, pp. 39-45. 11. Baluev D.G., Kaminchenko D.I. The political role of the "new media" in the Libyan conflict // Bulletin of the Nizhny Novgorod University Lobachevski N.I .2012, No 2 (1), pp. 307-313. 12. Levesque J. Social media “Tactical intelligence collection”: Spying and propaganda using Facebook, Twitter. February 15, 2012. 13. Gubanov D.A., Novikov D.A., Chkhartishvili A.G. Social networks. Models of information influence, control and confrontation. Moscow: Publishing House of Physical and Mathematical Literature, 2010, p. 228 14. Arquilla J., Ronfeldt D.F. Networks and netwars: the future of terror, crime, and militancy. Rand Corporation, 2001. 5 p. 15. Cebrowski A. K., Garstka J.J, “Network-centric warfare: Its origin and future.” U.S. Naval Institute Proceedings, January 1998, 10 p. 16. Revealed: US spy operation that manipulates social media, http://www.theguardian.com/technology/2011/mar/17/us-spy-operation-social-networks 17. Social Media in Strategic Communication (SMISC), http://www.darpa.mil/Our_Work/I2O/Programs/Social_Media_in_Strategic_Communication_ %28SMISC%29.aspx 18. Social Media in Strategic Communication (SMISC), http://www.darpa.mil/opencatalog/SMISC.html 19. Sykora M.D. et al. National security and social media monitoring: A presentation of the EMOTIVE and related systems / European Intelligence and Security Informatics Conference, 2013, pp. 172-175. 20. Batrinca B., Treleaven P. C. Social media analytics: a survey of techniques, tools and platforms // AI & Society, 2015, vol. 30, no. 1, pp. 89–116. 21. Pang B., Lee L. Opinion mining and sentiment Analysis // Foundations and Trends in Information Retrieval, 2008, vol. 2, no. 1-2, pp. 1-135. 22. Aliguliyev R.M. A new sentence similarity measure and sentence based extractive technique for automatic text summarization // Expert Systems with Applications, 2009, vol. 36, no. 4, pp. 7764–7772. 23. Karthik K., Kollias G., Kumar V., Grama A. Trends in Big Data analytics // Journal of Parallel and Distributed Computing, 2014, vol. 74, no. 7, pp. 2561-2573.

www.jpis.az 23 Problems of information society, 2016, №2, 24-33

Rashid G. Alakbarov Institute of Information Technology of ANAS, Baku, Azerbaijan [email protected] SUPERCOMPUTERS: CURRENT STATUS AND DEVELOPMENT PROSPECTS The article analyzes the current status of supercomputer technologies and their distribution dynamics on different indicators. The development dynamics of supercomputers in recent years is studied. Keywords: supercomputer, real productivity, theoretical productivity, green computing, big data, flops, cluster, computing systems, microprocessor. Introduction The establishment of information society in Azerbaijan has been accepted as one of the main priorities of the government policy. The main duties of establishing the information society include the development of legal fundamentals of this society, strengthening economic, social and intellectual potential of the country, formation of modern information-communication infrastructure, provision of information security, integration into global information space and other important issues. Supercomputers with high computing productivity and large memory are widely used to rapidly process the data that require complex computations and large volumes of data and occur during solution of indicated problems and deliver them to the users. At the same time, computing power of personal computers does suffice for solution of complex problems that require large computing and memory resources and occur in different fields of science: physical- chemical processes, nuclear reactions, modeling of global atmosphere processes in real time period, cryptography, geology, creation of new drug types etc. [1]. For this reason, supercomputers are widely used to solve such complex problems. Parallel computing systems and architecture of supercomputers Supercomputer is a computing system that exceeds in manifold the technical indicators (operating memory size, storage size of external disk enclosures, price, energy expenditure etc.) and computing power of existing computers. Supercomputer term was used, for the first time, by George Michael and Sydney Fairbank, employees of Livermore National Laboratory named after E.Lawrence in 1960s (California, USA). Supercomputers are developed on the basis of the parallel computing systems. Classification of parallel computing systems was proposed by American scientist G.Flynn for the first time in 1964 [1, 2]. Based on this classification, architecture of computing systems is created on the basis of the instructions flow and mutual relations of data flow. As known, instructions and data are processed consecutively in classic architecture of computing devices. Instructions and corresponding data are called to arithmetic - logic unit and implemented consecutively. But, function principles differ for computing systems. Classification proposed by G.Flynn is as following:  SISD-Single Instruction, Single Data;  SIMD-Single Instruction, Multiple Data;  MISD-Multiple Instruction, Single Data;  MIMD - Multiple Instructions, Multiple Data. SISD architecture matches the architecture of classic computing devices (proposed by Fon- Neimann). Such systems consist of one central processor; instructions are consecutively implemented on data. In such type of computing systems instructions and data (operands) written for solution of any problem are extracted from the memory one by one, implemented, and results are recorded into the memory in the end. In the second type of computing systems, operations are run on multiple data. There are tens of thousands of processes in such systems. Same operations can be run on different data

24 www.jpis.az

Problems of information society, 2016, №2, 24-33 using one instruction. Vector and matrix systems can be sited as examples for such systems. In these systems, it is possible to conduct the same operation on all elements of a vector or matrix using one instruction. One processor element is used for each element. Multiple instructions’ flow is executed on one data in the third type of computing systems. Supercomputers operating on this principle are not used in practice for time being. Architectures operating on MIMD principle are mainly used in parallel computing systems. Such systems provide parallel implementation of several instruction flows on different data. Computing systems with multiple architectures operating on MIMD principal – supercomputers were developed. Three types of architectures, based on MIMD principle, are widely used in the development of Supercomputers: SMP, MPP and cluster [3]. SMP (Symmetric multiprocessing) –general physical memory is used to provide the connection between the processors in computing system with symmetric multiprocessor archicture. Processors mutually connect with each other through general memory. Each of processors has an equal right while requesting any address from the memory. For this reason, systems with SMP architecture are called symmetric systems. SMP – systems are developed based on high-speed system buses (SGI Power Path, Sun Gigaplane, Dec Turbolaset etc). This system has following advantages: • use of general memory allows for rapid exchange of information among processors: • simple and universal for programming; • users are capable of using the memory in any volume; • there are multiple tools for automatic effective parallelization of problems; • simple exploitation. regular air conditioners working in room conditions are used in such systems; • not very expensive. Disadvantage of the SMP system is poor scaling of general memory systems. Indicated disadvantage does not allow wide use of these systems. The reason for difficulty of the wide scale use is the application of the general bus. This bus allows information exchange between two devices at the same time period. For this reason, request of several processors to the same part of the memory causes conflict situations and computing joints interfere with each other’s operation. Occurrence of such conflicts heavily depends on common bus speed and number of processors participating in the system. There are no more than 32 processors in systems with SMP architecture. Such systems are mainly used for creation of work stations and servers. MPP - Computing systems with Massively Parallel Processors are more widely spread in comparison with SMP architecture. In such systems, memory is physically distributed among processors. MPP system is developed out of processors and separate modules with memory using a commuter. In such systems, each model behaves like a fully functional computer. Only processors connection to each memory model can address them. In this architecture operating system can work in two options. In the 1st option, one of these modules is selected as the controlling computer (front-end) and operating system fully loaded onto it, simplified version of the operating system is loaded on other modules. In the 2nd option, operating system in fully loaded on each module. Computing system with MPP architecture can be scaled well. Synchronization of processors performance in such systems is easy. Supercomputers created on the basis of this architecture have high computing productivity. These systems combine tens of thousands of processors. ASCI Red and ASCI Blue Pacific supercomputers can be sites as examples for these. These systems have several disadvantages: • as general memory is not used, inter-processor information exchange speed is low; • each processor uses a small, local memory in its possession; • Special software providing inter-processor information exchange is used. Supercomputers with MPP architecture use MPI, PVM, BSPlib, Corba and other application packages in order to divide complex problem into sub-problems and distribute them

www.jpis.az 25 Problems of information society, 2016, №2, 24-33 among computing joints. NUMA (nonuniform memory access) hybrid architecture is used for the development of some supercomputers. This architecture combines SMP and MPP architectures. In NUMA, the computing system is created by combining SMP modules with local memory using high-speed communication network through general physical memory. Recently, supercomputers are developed on the cluster architecture. In the 46th rating table of 2015, 85.2% of supercomputers, produced in the world, , have the cluster architecture. Cluster – is a computing system that combines more computers (computing joints) using network technology (through common bus commuters etc.). Server, workstation, personal computer or blade-servers can be used as computing servers. If one the computing joint is out of order, then other computing joints overtake its operation, which is the advantage of the cluster system in comparison with other systems. Cluster system consists of separate modules. Each module is assembled of existing standard off the shelf assembly elements - processors, commuters, operating memory and disk memory device. The price of computers, based on this architecture, is very cheap. These supercomputers are assembled on existing compiling elements (off the shelf) – processors, commuters, operating memory, disk memory and external devices. Network technology (Fast/Gigabit Ethernet, Myrinet etc.) are used for connecting indicated modules. Cluster type computers are very simply to assemble, repair and control. Cluster systems are cheap, easy to install and operate. For this reason, this architecture is widely used to develop supercomputers worldwide. Linux, Unix, MS Windows and other operating systems are widely used for supercomputers. PVM, MPI, OpenMP and other application programs are widely applied in supercomputers for solution of complex problems [1, 4]. PVM (parallel virtual machine) was developed in 1991. Provides parallel implementation of a program divided into subprograms in multiprocessor computing systems (with cluster architecture) consisting of different types of computers (processors). MPI (Message passing interface) was developed in 1993. Provides parallel implementation of a program divided into subprograms in multicore and multiprocessor computing systems with distributed memory (with MPP architecture). Exchange among computing joints is carried out through exchange commuters (Gigabit Ethernet, Infoband etc.). Open MP (Open Multi Processing) was developed in 1997. Provides parallel implementation of a program divided into subprograms in multicore and multiprocessor computing systems with general memory (with SPM architecture). Supercomputers are used for solution of complex problems occurring in different fields of science indicated below [5]: . theoretical and experimental physics, high energy physics, quantum physics etc.; . quantum and molecular chemistry; . mathematical modelling of large systems; . chemical engineering, computational chemistry, creation of new materials; . engineering industry; . ecology; . genetics; . modelling of processing occurring on earth, ocean and atmosphere; . astronomy; . military, etc. Determination of computing productivity of supercomputers One of the main indicators of multiprocessor computing systems is their computational productivity. While compiling supercomputers’ rating table, their computing productivity is considered as the main indicator. The number of operations, executed in a second, is the computing productivity of the supercomputer. Productivity of supercomputers is evaluated through theoretical and real productivity. To calculate the theoretical productivity, we must

26 www.jpis.az

Problems of information society, 2016, №2, 24-33 multiply the productivity of one of the same type processors participating in the system by the number of processors. Real productivity is determined using special testing programs [6]. Real productivity comprises approximately 70-90% of theoretical productivity. In order to determine the productivity of supercomputers, Flop/s - floating point operations per second is used. Calculation of the theoretical productivity of supercomputers comprised of blade servers (microprocessor) is as following: Assumingly,, the supercomputer was assembled on two microprocessor Intel E5-2650 blade servers. Each microprocessor consists of eight cores operating on pipelining principle (consisting of eight blocks). Each microprocessor executes 8 operations during one stroke. If operational speed of the processor is 2 Ghz, then theoretical productivity of one core can be calculated as following: 8 Flop/stroke * 2 Ghz (stroke/second) = 16 GFlop/second. Full execution of any machine instruction in the microprocessor core is implemented within one stroke. Execution of one instruction approximately passes through the following stages: loading of instructions and data, decoding instruction, execution of instruction and finally, recording of the result. The afore-noted process is for the simplified condition of instruction implementation. In reality, more microprocessor strokes are used for operation implementation. Considering that microprocessors work according to pipelining principle, next instruction is provided at each stroke to the entrance of microprocessor. Each instruction processed in the microprocessor is passed on to the next stage after each stroke. Finally, the result of processed instruction is obtained at the exit of the pipeline. Thus, it is possible to execute several instructions in parallel during the float of one stroke at the same time. Processor implements not one, but eight instructions during one stroke. Productivity of the noted blade server is calculated as following: 16 GFlop/s/core * 8 core/CPU * 2 CPU = 256 GFlop/s. In order to obtain the noted productivity in blade servers, it is necessary to divide the solved problem (program) into 16 problems (programs) and direct each of them to the core. Let’s assume that any supercomputer is assembled of 4000 units of noted blade servers. Then, we can determine theoretical productivity of the supercomputer by multiplying the productivity of one blade server by their number. In the indicated case, theoretical productivity of the supercomputer is determined as following: 4000 * 256 GFlop/s = 1024000 GFlop/s =1024 TFlop/s= 1, 024 PFlop/s. Testing software is used in order to determine the real productivity of a supercomputer. LINPACK is the most popular testing software [1, 4]. LINPACK testing software was developed by J.Dongarra, employee of Argonne National laboratory operated by the University of Chicago,USA in 1979. This is testing software written to solve linear algebra formulas with NxN (1000x1000) unknown. These testing softwares are widely used to develop worldwide supercomputer rating tables. Analysis of current status of supercomputer technologies The first supercomputer (CRAY-1) was developed by an American scientist Seymour Cray in 1976. That supercomputer executed 160 million operations per second, its operating memory equaled to 8 megabytes and priced at 8.8 million US Dollars [4]. As of 1993, computer experts of Lawrence Berkeley National Laboratory in the US annually publish the rating table of the most powerful 500 supercomputers in the world (in recent years biannually – in June and November). In November 2015, the 46th rating table of supercomputers published, which is shown in Table 1 [7].

www.jpis.az 27 Problems of information society, 2016, №2, 24-33

Table 1. Supercomputers rating table Real Order N Name of organization and Computer names and Number of producti country manufacturers cores vity (TFlop/s) National Super Computer Tianhe-2 (MilkyWay-2) 1 Center (Guangzhou, (University of National 3 120 000 33 862.7 China) Defence Technologies) Oak Ridge National 2 Laboratory Titan (Cray Inc.) 560 640 17 590 (Oak Ridge, USA) Department of Energy’s 3 National Laboratory Sequoia (IBM) 1 572 864 17 173.2 (California, USA) … … … … … HLNR Hannover 100 Gottfried (CRAY XC40) 40 320 829.8 University (Germany) ...... … ...... CSIR Fourth Paradigm 300 Cluster Platform 3000 (HP) 17 408 334.4 Institute (India) ...... … … … University Regensburg 500 QPACE2 (Eurotech) 15 872 206.4 (Germany)

As you can see, Tihanhe-2 (Milky Way-2) supercomputer located at National Super Computer Center in Guangzhou city of China ranks first. Tianhe-2 consists of 3,120,000 cores, and has 2 33,800 TFlop/s (1012 Flop/s) computing productivity, its price equals to 290 million US Dollars. Comparison of supercomputers rating table for ten years demonstrates that the supercomputer ranking first in 2005 had only 136 TFlop/s computing power and the computer ranking 500th had 1.2 TFlop/s productivity. Productivity of the computer ranking first in 2015 exceeds the productivity of the supercomputer of the same rank in 250 times. It must be noted that productivity of the supercomputer ranking first ten years ago is 164 Tflop/S less than the productivity of the supercomputer ranking 500th in current table. This is an indicator of the rapid increase of productivity of the supercomputers. The supercomputer ranking 5th has not changed in the past two years. This demonstrates that the rational use of supercomputers, rather than their computing power, is brought into the forefront. Currently, the productivity of 82 supercomputers is higher than 1 Pflop/s. Analysis of distribution of supercomputers among countries demonstrates that 39.8% of supercomputers ranking in the rating table belong to USA (Figure 1).

28 www.jpis.az

Problems of information society, 2016, №2, 24-33

Figure 1. Distribution of supercomputers among countries. Broad changes have taken place in the architecture of supercomputers manufactured during the recent years. Formerly manufactured supercomputers were made on the basis of MPP and SMP architectures. Currently manufactured supercomputers increasingly have cluster type architecture (Figure 2). Supercomputers with cluster architecture were created on the module principle; this allowed using them more efficiently. Supercomputers with cluster architecture are less costly than supercomputers with other architectures, which has stimulated their mass production. Conducted researches demonstrate that majority of supercomputers (46%) are used in an industrial field. At the same time, 25% are used in scientific research, 21.8% in education works, 6% in government projects, and 1.2% in manufacturing facilities (Figure 3).

6, Industry 30 (6%) (1.2%) 74, Scientfic (15%) research 230, Cluster 109, Education (21.8%) (46%) MPP 426, 125, Government (85%) (25%) Manufacturing facilities

Figure 2. Distribution of supercomputers by Figure 3. Distribution of supercomputers architecture by the field of use Out of 500 computers shown in the rating table, 155 were assembled using HP equipment, 69 using Cray Inc. and 49 using Sugon equipment. Supercomputers assembled based on IBM (45), SGI (31), Lenovo (25) and Bull (21) companies’ equipment rank in lower positions. Tianhe- 2 supercomputer that ranks first was assembled by NUDT (National University of Defense Technology). Distribution of supercomputers by manufacturing companies is shown in (Figure 4)

www.jpis.az 29 Problems of information society, 2016, №2, 24-33

Picture 4. Distribution of supercomputers by manufacturing companies Linux based operating systems are used by 487 supercomputers out of 500, and. 212 out these supercomputers are in the Western hemisphere (North America – 205, South America – 7). In Asian countries, there are 174 supercomputers. Europe ranks third with 108 computers. Australia has 6 supercomputers in its possession. Communication network providing connection among computing joints of supercomputers are installed on the basis of Infiniband in 47.4% (237), 10G in 23.8% (119), Individual in 14.8% (74), and Gigabit Ethernet in 12.4% (62). 432 out of 500 supercomputers were assembled based on Intel (86.4%), 26 based on (5.2%) Power, 21 based on (4.2%) AMD and 5 based on (1%) Spark microprocessors. Development perspectives of Supercomputers Currently, supercomputer technologies are widely used to process and record Big Data and solve complex problems that require great computing and memory [8]. Based on the researches, conducted by IBM company’s experts, 15Pbite of data is produced every day (scientific articles, pictures, audio-video files, social network report etc). Tianhe-2 (China) supercomputer with high productivity has 40 Pbyte memory storage, while Titan (USA) has 40 Pbyte memory storage. The memory storage of supercomputer located in Utah National Security Agency (Utah, NASA – USA) equals to 1 Ybyte (yottabyte) - 1024. Noted memory storages are overly large. For the comparison, it needs to be noted that all books written to date can be fit into 400 Tbyte memory. Development in different fields of science will increase the volume of data to be processed exponentially. In the future, this will lead to use of supercomputers with high computing productivity and memory storage. The computing power of supercomputers used for simulation of the mathematical model created for determination of atmospheric processes equals to 4 Tplops (during 4 hour computational period). Supercomputer with computing power of 1 PFlop is required to research structural characteristics and stability factors of DNA. Development dynamics of the supercomputer, to be manufactured in 2020 (To rank 1st in Top 500), is forecasted as following (Table 2) [9].

30 www.jpis.az

Problems of information society, 2016, №2, 24-33

Table 2 Development dynamics of supercomputer productivity Theoretical Real productivity Years productivity (PFlop/s) (PFlop/s) 15.11.2015 84.7 137.1 15.11.2016 158.6 256.7 15.11.2017 296.8 480.6 15.11.2018 555.6 899.6 15.11.2019 1.040 1.684 15.11.2020 1.947 3.152

Currently, works are carried out in Oak Ridge National Laboratory, Japan, location of Titan supercomputer ranking 2nd in the rating table, to develop a new supercomputer titled Summit with planned commissioning in 2017. The power of the newly developed supercomputer is planned to exceed by 5 times and equal 150 PFlop/s [7]. Works are conducted towards the development of a new Aurora supercomputer based on Mira supercomputer with 10 PFlop/s computing productivity, ranking 5th in the rating table and located at Argonna National Laboratory, USA. The computing power of the new supercomputer will be 180 PFlop/s, and will be put into operation in 2019. Energy consumption of supercomputers is overly great. For example: Energy demand of Tianhe-2 is 17.8 MW, energy demand of Mira is 8.6 MW etc. Annual energy expense of Tianhe- 2 supercomputer is 24 million dollars. Green Computing technologies are widely used in order to eliminate the noted problem [10, 11]. Green computing is ecologically oriented information- telecommunication technology. Computer products created upon this technology must meet requirements, such as low use of dangerous materials, low power consumption, long term exploitation period of equipment, cost effective utilization without environmental damage etc. With the help of this technology, next generation Summit supercomputer developed on the basis of Titan supercomputer, located at Oak Ridge National Laboratory, will have 5 times more theoretical productivity by consuming 1.21 times more power. The same is applicable to Mira located at Argonne National Laboratory United States, and next generation Aurora. Thus, when theoretical productivity of Mira is 3 945.00 kW using 3 945.00 kW of power, Aurora will consume 2.7 times more power and increase its productivity to 180 PFlop/s (18 times more). New cooling systems, reducing the power consumption and nanotechnologies which create the element base, are widely used to increase the computing productivity of supercomputers. Currently, 22 nm (nm –one billionth of a metre) and 14 nm technologies are widely used in the development of microprocessors used in supercomputers. A number of transistors in microprocessors, developed upon these technologies, will equal to 2-4 billion. In coming years microprocessors will be developed on the basis of 7 nm and 5nm technologies. Computing productivity of supercomputers, based on these microprocessors, will equal to tens of EFlops (exaflops). It is impossible to reduce the dimensions of transistors in microprocessors following 5 nm technology [12, 13]. For this reason, new technologies (quantum, nanoparticles etc.) will be used to create microprocessors in the coming years. Conducted researches show that the part of the power used in data centers is used for cooling of the system. Currently, three types of cooling systems are more widely used:  Air Cooling System;  Water Cooling System;  Immersion Cooling System.

www.jpis.az 31 Problems of information society, 2016, №2, 24-33

In air-cooling system, the cold air is supplied to the equipment through the grating of the raised floor and returns back to the conditioner from the ceiling of the room. This cooling system consumes 40-50% of overall power consumed by the data center. In water-cooling system, water circulates in the pipes surrounding the entire system and cools the equipment. This cooling system uses 20-25% of the overall power consumed by the data center. In immersion cooling systems, servers are immersed in dielectric liquid. This cooling system uses 3-5% of overall power consumption of the data center [14]. This system is relatively expensive in comparison with other systems. In spite of this, cooling system of the supercomputers in first three ranks of the supercomputer rating table of power saving supercomputers were created on the basis of the immersion cooling systems. Majority of financial supply, necessary for placement and operation of supercomputers in data processing centers, are spent on supercomputer cooling systems. In order to reduce these expenses, application of Free Cooling Systems requiring low expenditure is considered in the near future. Data processing systems, developed on the noted cooling systems, will be mainly located in regions and countries with cold climate. Cost effective cooling systems will reduce the power consumption of data processing centers by 40% [10]. Iceland is expected to play an important role in data-center industry in the future. Several major companies are planning to locate their next data centers in Iceland. Iceland is planning to take important steps in order to develop this sector. Supercomputer located at the ANAS Institute of Information Technology Data Center has 15 TFlop/s computing productivity and 300 TByte memory. Supercomputer renders electronic mail, Internet and hosting services for institutes and organization of ANAS. In addition, supercomputer is used to solve complex problems that occur in the institutes and organization of ANAS, and require large computing and memory resources. Purposeful works are carried out in order to create a Supercomputer center at the Ministry of Communications and High Technologies of the Republic of Azerbaijan, and it is planned to put the center into operation in 2016. Conclusion Classification of parallel processing systems and architecture of supercomputers were analyzed in the article. Current status of supercomputer technologies and their distribution dynamics, based on different indicators. was analyzed. Development dynamics of supercomputers in the near future was studied. At the same time, use of supercomputers in data processing centers was analyzed. Issues related to use of new technologies for increasing the productivity of supercomputer and reducing the power consumption of supercomputers were reviewed.

This work was carried out by the financial support of Science Development Foundation under the President of the Republic of Azerbaijan – Grant № EİF-2014-9(24)-KETPL- 14/02/1 References 1. Voevodin V.V., Voevodin Vl.V. Parallel computing, Saint-Petersburg: BHV, Petersburg, 2002, 608 pp. 2. Tanenbaum E. Computer architecture. SPB.: Petersburg, 2003, 704 pp. 3. Kornev V.V. Computing systems. M.: Gelios ARV, 2004, 512 pp.

32 www.jpis.az

Problems of information society, 2016, №2, 24-33

4. Kornev V.D. Parallel computing in MPI. Novosibirsk: ICMMG SB RAS Russia, 2002, 84 pp. 5. “Tasks for supercomputers”, http://www.parallel.ru /research/ apps.html 6. Voevodin. V.V., Supercomputers: yesterday, today, tomorrow, http://www.nkj.ru/archive/7365/ 7. http://www.top.500.org/lists/2015/11 8. Materials of Fifth Moscow Supercomputer Forum, 21.10.2014 http://www.ospcon.ru/node/107252 9. Abramov S.M., Lilitko E.P., Development status and perspectives of computing system with ultrahigh productivity, http://paco2012.ipu.ru/procdngs/ P103.pdf 10. “What is green computing or green information technologies”, http://www.nature- time.ru/2014/07/zelenyie-vyichisleniya-ili-zelenyie-informatsionnyie-tehnologii/ 11. http://www.green500.org/lists/green2015/11 12. Landauer R. Irreversibility and Heat Generation in the Computing. IBM Journal, July. 1961, pp.183191. 13. Bennet C.H., Landauer R. The Fundamental Physical Limits of Computation. Scientific American, July 1985, pp. 3846 14. Zhyrkov A. Supercomputers: development, trends, application. Review of HPC solutions of Eurotech. Modern automatization technologies, 2014, № 2, pp.1620.

www.jpis.az 33 Problems of information society, 2016, №2, 34-45

Rasim M. Aliguliyev1, Makrufa Sh. Hajirahimova2, Aybaniz S. Aliyeva3 1,2,3Institute of Information Technology of ANAS, Baku, Azerbaijan [email protected], [email protected], [email protected] CURRENT SCIENTIFIC AND THEORETICAL PROBLEMS OF BIG DATA A very large size of digital data has been generated in the world in view of the technical and technological development in the recent decade. As a result, a notion of “big data” has emerged; nowadays, it has become an important topic that is broadly discussed in newspaper and journal articles, blogs and etc. Alongside with creating new prospects for the modern society, “Big data” has also brought about some problems for researchers. Unlike the mass media and the business sector, the notion of “big data” is reviewed as a scientific-research object in this article, and a short comment on “big data” concept is provided. The main factors underlying its development as a research direction are presented. Moreover, current scientific and theoretical issues in the focus of researchers are also analyzed. Key words: big data, big data analytics, audio analytics, video analytics, social media analytics, visualization, security. Introduction Starting from the beginning of the 21st century, digital data generated by technologies – computers, mobile phones, Internet, sensor networks, artificial satellites of the Earth, cosmic telescopes, cloud computations and etc. experiences an exponential growth annually. This situation is characterized as an “information explosion” in the society. In this sense, if 5 exabyte of data was generated in total during the period from the existence of humanity till 2003, this indicator constituted 2,7 zetabytes in 2012; this figure is expected to increase by 40% in each next year and reach 44 zetabytes by 2020 [1]. With the rapid increase of digital data, a notion of “big data” has emerged reflecting the new age in the processing, storage and use of data [2]. This notion is intended to specify the mass of big data which cannot be processed by the current management methods and intellectual analysis tools in terms of volume and complexity [3]. As a phenomenal event, big data has attracted each segment of the society in the short period of time. It is due to the reason that big data (BD) has a large potential of revolutionary changes in management and business, large profit generation in enterprises, development and realization of scientific ideas in various fields [3,4]. The data analysis, and the extraction of knowledge and useful information from those play an important role in the realization of new scientific discoveries, in well-founded decision-making in organizations, national security and medicine. However, alongside with giving an impetus for the economic development of the society, the BD has also posed several problems to scientific community and created new research paradigms [5-11]. In order to embody the potential of BD, several technical and technological problems must be resolved first. Several problems (computation, comprehensiveness, storage, incorrect correlations and etc.) emanating from the characteristic features (large volume, high velocity, variety) exist which require new scientific point of view, attitudes, modelling, mathematical methods, optimization tools and etc. These problems demand more effective statistical and intellectual analysis tools, architecture attitudes providing comprehensiveness, unified infrastructure and tools from researchers in order to obtain the needed information and knowledge. In this regard, the investigation of the most topical scientific and theoretical problems of the big data is of large importance.

34 www.jpis.az

Problems of information society, 2016, №2, 34-45

Big data concept In general, when the history of data development is reviewed, it can be seen that a technology used in data management analysis – “data base” management systems have emerged in the 70’s of the past century [12]. It can be said that the concept of the “data base” was established ever since. However, mainframes (general-purpose universal electronic-computation machines) were not able to maintain the adequacy for the storage and processing as a result of the increase in data volume. In subsequent years, “parallel data base system” was proposed for the solution of the problem [13]. The architecture of these systems is based on the use of clusters (each processor in cluster consists of the processor, memory and the disc). It is worth mentioning that the “parallel data base system” was quite popular till the end of 90’s of the past century. However, with the increase of the varieties of Internet services, the storage and processing of big data has also increased. Fundamental changes in computation architecture and expandable processing mechanisms are required in the solution of problem. Encountered the BD problems, one of the giants of information technologies (IT) sector Google corporation have generated File System Google [14] and MapReduce [15] software and hardware platform for the data management and data analysis at the Internet scale. Open code Apache Hadoop and Hadoop Distributed File System [16, 17] program software, also NoSQL data base was developed which have established the big data technologies. This technology is considered as the most accurate choice for the storage and management of large-scale data. It is because this technology is more effective in technology clustering issues, especially in the evaluation of the rating of web-pages. At the same time, it has also defeated the drawbacks of data warehousing systems, and enabled the collection of required information by using more complex analytical tools [11]. Several research works are available regarding the history and the review of the big data term [2, 3, 5–9, 18-20]. Investigations show that the big data is one of the terms with known history. Such that, the term of big data was firstly used by John Mashey, the expert on computer sciences of Silicon Graphics computer enterprise in 1998. This term is later encountered in 2000 – in a research work published in academic environment by Francis Diebold, a professor of the University of Pennsylvania and one of the leading researchers of the big data term [2]. His further work reckons that the term is firmly established, and has become a rigorous research direction rather than an event or phenomenon [18]. Notwithstanding, the term has gained popularity after an academic article published by Clifford Lynch, the professor of the University of Berkeley [21]. Regardless the fields applied, some general features pertain to the big data. These features can be divided into three main groups by reflecting the main problems of BD: volume, velocity and variety. This is also called “3V” in English-speaking sources. These features are broadly commented in several scientific sources [3, 5–9, 20, 22, 23]. The first model enabled to specify the BD and to distinguish it from other data, which was presented by Doung Laney, the analytics expert of Gartner enterprise in 2001 [24]. He has forecasted a tendency in electronic commerce: the higher importance and complexity of data management; thereafter, he specified the volume, transmission velocity and the variety of the data as main problems in data management. The considered features constitute the main concept of big data technologies in general. This concept reflects the idea of more efficient use, storage and extraction of more valuable information by the analysis of very large-volume data gathered at high velocity and from various sources. This characteristics are commented in [3] as following: Volume. Volume is the main feature of BD and the quantitative indicator of the data. At present, this indicator is measured by the volume of terabytes till zetabytes. The volume problem primarily causes a problem of storage which requires large-scale storage and distributed processing. Velocity. Two cases are considered here. First, new data is generated at high velocity, the existing data are updated and collected. Second, as the volume increases, very high velocity is

www.jpis.az 35 Problems of information society, 2016, №2, 34-45 required for the processing. The velocity is regarded as a problem of time and interpreted as the capability of existing processing technologies to analyze the data in real time. Variety. Variety is one of the natural features of BD. The majority of information enters from different sources (e-mail, social networks, web-sites, sensors and etc.) at different formats, and different indexation scheme is applied. It is not an easy task to compile, jointly process and convert them into an appropriate format for the analysis. Considering the veracity of the data and the value of BD, IBM and Oracle enterprises have added fourth “V” (veracity) and fifth “V” (value), respectively. Veracity. By veracity, the quality of the data (complete, incomplete, conflicting and etc.) is understood. The quality of the data can change at large volumes which may affect the variety and the outcome of analysis. Value. The data must possess the capability to generate the value. If BD does not generate value, it becomes “information dump”. The constant attention to big data by business sector is due to its ability to generate value. Hence, this factor is evaluated as a marketing feature [1, 4]. It is because the value of information is determined by how we utilize it. It must also be mentioned that the number of “v”s is constantly being increased by the experts in recent times. The factors of formation of Big data as a research paradigm Nowadays, the information abundance called big data is indeed present. However, it must not discourage people; on the contrary, it must be considered as a natural resource. Because, these natural resources possess the comprehensive knowledge, which can give an impetus for scientific discoveries. A new generation of analytical technologies is required for the value generation in the society and the business sector by using these resources at maximum. In this regard, the BD topic has attracted large attention of decision-making persons and politicians in government bodies, as well as the business sector and scientific researchers, and as mentioned, has become a new research direction. In the first instance, it must be mentioned that various conferences, symposia, seminars and forums are held by notable international organizations, scientific institutions dedicated to different aspects of very large-volume information processing. The main discussion topics of those events are: Big Data architecture, Big Data management, Big Data modelling, Big Data analytics, Big Data toolkits, Big Data open platforms, Big Data as a Service, Big Data in Business Performance Management, Big Data Analytics in e-Government and Society, visualization, security, Big Data algorithms and etc. [3]. The dedication of special issues of scientific and mass journals to the topic of big data and the publication of new academic journals elucidating the scientific and theoretical problems of BD starting from 2014 is one of the main factors emphasizing the importance of this field as a rigorous scientific direction. Fundamental scientific-research is being carried out in popular world scientific centers on the topic of big data collection and processing, storage, architecture, analytics, security, visualization and so forth. [3]. Starting from 2013, “data science” started to be taught as an academic module at bachelor, master and doctoral levels in several leading universities of the world: University of Dandee (Scotland), University of Auckland (New Zealand), University of South California, University of Washington, University of Berkeley, University of New-York, Imperial College London and etc. [6]. Such programs aim to provide a fundamental training such as the computation models for the maximum use of the potential of large-volume data, the mathematical methods for modelling and forecasting, architecture, contemporary programming methods, data collection, storage and analysis. At present, the number of these universities is constantly increasing. The program is being taught in Sabanci University in Turkey, Higher School of Economics in Russia and etc. at

36 www.jpis.az

Problems of information society, 2016, №2, 34-45 master levels. It is worth mentioning that, the “data scientist” specialty is one of the promising popular specialties. Big data technologies and corresponding fundamental research have become the main scientific research directions of these education centers. The bibliometric analysis in several leading science databases of the world is one of the main factors emphasizing the development of BD as a research topic [8-11, 25]. In this case, the topic development can be considered in terms of bibliometric indicators such as time, space, scientific fields, the number of published research works, type (book, article, conference proceeding and etc.) and etc. For instance, if only one research work could be found as a result of search on “big data” keyword in 2008, the number of scientific-research works has shown an exponential increase, starting from 2012. First places in geographical distribution of research works are occupied by USA, China, India, Great Britain, South Korea and etc. countries. The research results carried out in Google Scholar and Springer databases are similar to [10, 25] as well. The dynamics of research works on years are given in the graph below (Figure 1).

Figure 1. Distribution of research works on big data in Google Scholar and Springer databases The distribution of works according to the document the type and field of science in Springer database are given in Figure 2 and Figure 3 respectively. As seen from the Graph, the research works related to computer sciences, engineering, management, medicine, mathematics and social sciences occupy a large share in documents. Bibliometric indicators emphasize an increasing importance of the data in various scientific fields and the formation of the big data as a scientific direction once again.

www.jpis.az 37 Problems of information society, 2016, №2, 34-45

Figure 2. Distribution of research works according to the type of documents on big data in Springer database

Figure 3. Distribution of research works on big data in Springer database

38 www.jpis.az

Problems of information society, 2016, №2, 34-45

In general, big data has encouraged the revolutionary changes in the methodology of scientific research, scientific thoughts and methods. The extraction of new knowledge from data has established scientific investigations based on the intensive processing of this resource [6, 11]. The Turing Award winner Jim Gray has noted four scientific paradigms. Hence, the science was empirical thousand years ago, theoretical in hundreds centuries thereafter, and computational in recent decades; as the fourth paradigm, he has named the science based on the intensive processing of the data – the electronic science distinguished from computational science [26]. J. Gray reckons that the systematic solution of the majority of the complex global challenges, encountered by the humanity, can only be proposed by the fourth paradigm of the scientific research. According to this paradigm, the fields of science based on the intensive processing of the data has started to develop, whereas the scientific investigations become to be dependent on the data acting as a main source of the modern scientific discoveries [26]. The development of the fourth paradigm is stimulated by the large-volume data; various scientific fields are managed with the data at high level [9]. For example, fields of science such as astronomy [27], social evaluations [28], bioinformatics [29] computational biology, meteorology and etc. are based on the large-volume data processing. The interdisciplinary field named as “data science” gains its positions gradually [6, 30, 31]. The research topic of this field is the big data oriented to the extraction of generalized knowledge from the data. The data science is interlinked with several disciplines including informatics, mathematics, social sciences, network science, economics and etc. Hence, alongside with mathematics, statistics and informatics, the fundamentals of BD processing and analytics are constituted by various methods and theories, including the probability theory, image recognition, neural networks, intellectual analysis [32,33], machine learning [34], signal processing, natural language processing, forecast analysis, visualization [35,36], optimization, statistical methods [37], programming, engineering, the modelling of uncertainties, high productivity computations and etc. Scientific and theoretical problems of Big data Surely, the achievements of scientific research conducted in relation with the revelation of mysteries, surrounding the humanity, is a result of the efficient application of BD capabilities [9]. However, the revelation of knowledge unknown by BD and the decision-making, based on such knowledge, is a complex issue in terms of the data organization and processing – a new paradigm called “big data computing” [11]. This new paradigm comprises the methods and models of large-scale storage, processing and computation. That is, new scientific approaches and sophisticated analysis tools are required for the solution of the problems such as compilation and management, storage, security, search, analysis (generation of analytical reports and visualization, forecasting) and etc. of texts, images, audio, video, and other types of unstructured information with volume of hundreds of terabytes which carry exceptionally useful information and not processed by common relational databases [9]. In [38], the problems pertaining to BD are divided into three groups: 1. Data issues are related to the volume, velocity, variety and veracity. Thus, the data volume is increasing in comparison with past periods and existing tools are not capable to process them. Data may be in different format (text, sensor, audio, video, graph and etc.). Data is generated as constant flow and it is needed to extract useful information from them in real time. The incompatibility in databases may complicate the process of data processing and management. The quality of data may change and this change may impact the outcome of the analysis. 2. Processing problem includes the collection of valid information for analysis and search for solution compatibilities from various sources. Moreover, this includes the analysis and the presentation of access, that is, the visualization of the results in most appropriate manner comprehensible for a person.

www.jpis.az 39 Problems of information society, 2016, №2, 34-45

3. Data management is the last of the proposed classifications. The management problem is mainly related to the preparation of data for analysis, storage, collection, the provision of data privacy and security, that is, the management of data lifecycle in general. It is the provision of correct use of data. This is regulated by information security policies and rules considered at national and international levels. Problems of big data analysis. In general, the rapid increase in data volume and the demand for their analysis in real-time regime has led to the establishment of Big Data Analytics which is considered one of the main problems of BD. This is the process of detection of hidden regularities, unknown correlations and other useful information in large-volume data for the optimal decision-making. While Big data Analytics is applied to larger and more complex massives, Discovery Analytics and Exploratory Analytics terms are used alongside. Regardless of how it is named, the essence of analytics is not altered – to establish a feedback providing the information on different processes for decision-making people [3]. Three types of BD analytics are mentioned in [39]: big data descriptive analytics, big data predictive analytics, and big data prescriptive analytics. Big data descriptive analytics is engaged in questions “what happened?”, “why has it happened?”. Big Data predicative analytics answers the question “what will happen?”. Big Data prescriptive analytics not only shows “what will happen”, “when will it happen”, but also “why will it happen”. In general, BD analytics is one of the main problems of BD. This problem is related to the features, existing analysis models and methods and the limitations of data processing systems [9]. In [6, 40], the integration of various types of data, the volume, scaling, security, and incompatibility of data are indicated as main problems of analytics. Certainly, complex data analysis methods also exist. However, the majority of traditional methods of data analysis are not capable to dynamically adapt to different situations and to scale; those do not operate in parallel computation environment. The analysis of unstructured information such as text and audio, the collection of information from various sources and their integration is a serious problem. That is, the majority of existing methods are not sufficient for large and complex data [41]. It is known that an analysis allows to find the correlation among different parameters, features, events and etc., to classify, to prepare analytical reports and forecast in this basis. From this point of view, modern technologies must allow to convert the information into new knowledge or obtain the knowledge. The storage, processing and the analysis of BD require architectural attitudes and unified infrastructures providing the computation power and scalability. Such big data mining methods must be applied, which are able, in the first instance, to detect the changes in data. In short, there is a need for the conduction of practical and theoretical research for the creation of distributed version of existing models and methods or the development of new ones. Main directions of BD analytics are associated with text, video, audio and social media analytics [8]. Text analytics – extracts the knowledge by detecting the previously unknown relations and correlations from natural language texts with the help of methods pertaining to data mining class [32, 33]. The classification and clustering, information extraction, summarization are the main problems solved by text mining. Essentially, text mining employs information search algorithms, as well as machine learning algorithms of linguistic and statistical methods for more comprehensive text analysis [8, 42]. Video analytics – comprises the monitoring and the analysis of video flow, and the extraction of useful information; it is a serious problem from the point of view of big data. Video-information is the main form of digital information and observations, and has a large volume. The analytics of this type of recordings is at the initial stage in comparison with other sorts of data analysis. The analysis of video-information is one of the most complex issues posed

40 www.jpis.az

Problems of information society, 2016, №2, 34-45 to researchers. The main problem here is the information loss occurring as a result of loss in frame frequency and the precision of images [8]. Audio analytics – is the method of analysis of unstructured audio data and the extraction of useful information from those. These methods allow to improve the quality of services provided to customers, and to control the conduction of realization issues such as privacy and security. Its main problems are related to speech recognition, noise and etc. [8]. Social media analytics – carries out structured and unstructured data analysis of channels; it is used for forecasting the user behavior. The content in social media is usually large-volume, noisy and dynamic. Hence, the problems mentioned in text, audio and video analytics, as well as BD transmission also pertain to social media analytics [8, 40, 43, and 44]. Architecture problems of big data. At present, no broadly accepted architecture is present for BD analytics. The main functional components of big data architecture include data extraction, stream processing, information extraction, data quality/uncertainty management, data integration, data analysis, data distribution, data storage, metadata management, data lifecycle management and privacy. [19, 45] presents the review of existing ‘etalon’ architectures and platforms for large-volume data analysis such as Hadoop, MapReduce, NoSQL and etc. For now, it is not specified how the optimal architecture of analytical systems must look like for the simultaneous processing of retrospective and real time data. Problems of big data processing and storage. Package provides the package processing; Stream Computing provides the analytical processing of the data regularly updated in real time regime and allows for forecasting, more rapid analysis and decision-making [9]; Data-intensive computing: is oriented to parallel computations in the processing of terabytes and petabytes of data. The necessity of data processing in petabytes has led to the emergence of data–intensive computing approach, which reckons that “not the computations, but the data is the greater wealth” [3, 9, 46]. Big data has radically altered the methods of data processing and storage, storage devices, storage architecture and data access mechanisms. The storage devices must be capable of providing the accessibility and operative analysis of large-volume data [9]. At present, DAS (Direct Attach Storage), NAS (Network Attached Storage), SAN (Storage Area Networks), HSM (Hierarchical Storage Management), ILM (Information Life-cycle Management) technologies are broadly used for the solution of the storage problem which are capable to carry out the transmission of information among devices [3, 6]. However, as for the level of large-scale distribution systems, the drawbacks and limitations of these storage architectures of data emerge. Recently, the application of grid and cloud computing technologies, which carry out the expansion of the memory capacity of devices, clustering and virtualization of computation and memory resources has almost eliminated the problems in storage [3, 9]. The cloud computing is one of the exceptionally successful approaches in storing and performing the big data computations. On the other hand, cloud storage creates a problem of data security in terms of the control over data completeness [9]. The evaluation and optimization of energy efficiency of BD processing and storage is of rigorous scientific-research importance [7, 8]. Problems of big data visualization. One of the main issues in BD analysis is the representation of results – visualization. Overall, the data visualization is one of the most simple and natural methods in data processing and analysis. This method allows a person to get familiar with presented information instantly and to make optimal decisions by correctly assessing the results [36, 35]. The visualization of large-size and large-volume data is not an easy task. Considering the features of BD, the process of visualization of such data encounters the following problems [35]:  visual noise. This problem occurs due to the excessive interconnectedness among the objects in dataset. By noise, the shrinking and loss of separate objects rather than the

www.jpis.az 41 Problems of information society, 2016, №2, 34-45

deterioration and distortion of data is understood. This complicates the extraction of useful information from total view and additional processing is needed.  large image reception. Human brain is capable of receiving a visual view at some extent. Although the level of perception of graphic data visualization is higher than table visualization, some limitations exist. Hence, after a certain level, a human loses the capability to extract additional information from already loaded visual data. Surely, the methods of visualization are limited with the capabilities of technical devices providing the output view of data. No matter how advanced equipment we use, we encounter the limitations of human perception. That is, the methods of data visualization are not solely limited with the capabilities of devices, but also with physical perception of human. The data filtering – shrinking approach is used in order solve the mentioned problem.  Information loss. This is the problem emanating from the solution of perception of visual noise and large image. The attitudes applied in solution of mentioned problems reduce the data used at the end, but also lead to the emergence of the problem of information loss. It is because the methods of shrinkage of visual information carries out the aggregation and filtering of data according to one or several criteria based on the similarity of objects. These approaches may mislead analysts, and thus rather important and interesting issues may be ignored. Moreover, the process of data aggregation for obtaining the accurate and necessary information may require substantial time and computation resources.  high performance requirements. Graphical analysis is not only confined with the static visualization of images, but dynamic visualization is used as well; in this case, a subtle problem occurs in static visualization. There emerges a need for process performance at a certain velocity of visualization. It is due to the reason that, substantial time and computation resources are required for the filtering of large-volume data during the analysis process.  high rate of image change. As seen from subtitle, this problem is related to the high rate of change of images. That is, a person simply cannot react to the rapid change of data or their intensity on the screen during observation. The reduction in the rate of changing images is not able to provide the desirable effectiveness of the process. However, the speed of human reaction creates certain problems in this process. It is necessary to develop new methods and technologies, as well as the qualified personnel for the elimination of such problems. Security problems of big data. Overall, data security is considered to be an important issue at any time. New challenges have appeared for the information security with the emergence of BD. These problems may be approached in two aspects: 1) the application of big data analytics for information security; 2) information security in big data analytics [47, 48]. Both aspects are one of the topical problems posed to researchers. Such that, the application of big data technologies has demonstrated the obsoleteness and inadequacy of existing security models applied 15 years ago for today. The more digitalized the information and the more information added, it is more accessible and the number of users is higher. As a result, the incidents related to information security emerge such as the theft, distortion of information and network hack, the capture of personal information easily by malicious subjects. The analysis of individual information without the consent of those individuals is unacceptable both ethically and legally; it is a serious problem in terms of security and privacy [49]. The features of BD, such as the variety and velocity, have exacerbated the security and privacy problems. First, the size of big data is excessively large in relation to existing security approaches. This, in turn, complicates the measures in security field. On the other hand, big data is stored in distributed form and network threats may increase such threats [9]. The large-scale

42 www.jpis.az

Problems of information society, 2016, №2, 34-45

“cloud” infrastructure, the variety of data sources, collection of stream information and the migration of large-volume information into “clouds” have also revealed caveats in security systems. Therefore, traditional security mechanisms are not sufficient in situations of BD expansion. At the same time, data stream requires quite flexible and rapid security solutions [9, 47-49]. The problems constituting the main directions of scientific research, such as the detection of cyber-attacks; information systems security, management of security risks, information risk assessment and so on, are one of the important problems challenging the researchers in the solution of problems similar to BD [47]. Conclusion As a valuable source of knowledge, big data has become the most debated topic and new multidisciplinary scientific research direction in the field of information technologies by attracting the state, business and scientific communities throughout the world. The main directions of scientific research works are constituted by science-intensive problems emanating from natural features, such as volume, velocity and the variety; the latter form the basis for the big data concept. The problem groups include various issues starting with the data collection and ending with the presentation of results to user. For this reason, the problems must reach their scientific solution related to the processing and management, storage, security, search, analysis and etc. of the text, images, audio-video and other type of the unstructured information in terabytes and exabytes. The development of methods such as data mining-class methods which are more effective in problem solution (associative rules, regression, classification, clustering and etc.), artificial neural networks, machine learning, optimization, as well as genetic algorithms, image recognition, predictive analytics, imitation modelling, statistical analysis, and the visualization of analytical data, are one of the main issues posed to researchers. References 1. The digital universe in 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East. Study report, IDC, November 2014, http://www.emc.com/leadership/digital- universe/index.htm 2. Diebold F. Big Data Dynamic Factor Models for Macroeconomic Measurement and Forecasting / Discussion Read to the Eighth World Congress of the Econometric Society, Cambridge: Cambridge University Press, 2000, pp. 115-122. 3. Aliguliyev R.M., Hajirahimova M.S. “Big data” phenomenon: problems and prospects // Information Technologies problems, 2014. №2, pp. 3-16. 4. Big data: The next frontier for innovation, competition, and productivity. Analyst report, McKinsey Global Institute, May 2011, http://www.mckinsey.com 5. Fan J., Han F. & Liu H. Challenges of Big Data analysis // National Science Review, 2014, vol. 1, no. 2, pp. 293–314. 6. Chen P.L., Zhang C.Y. Data-intensive applications, challenges, techniques and technologies: A survey on Big Data // Information Sciences, 2014, vol. 275, pp.314–347. 7. Chen M, Mao S, Liu Y. Big data survey // Mobile Networks and Applications, 2014, vol.19, no.2, pp.171–209. 8. Gandomi A., Haider M. Beyond the hype: Big data concepts, methods, and analytics //International Journal of Information Management, 2015, vol. 35, pp. 137–144. 9. Jina X., Benjamin W. Waha, Chenga X., Wanga Y. Significance and Challenges of Big Data Research, 2015, vol.2, no.2, pp. 59–64. 10. Halevi G. The Evolution of Big Data as a Research and Scientific Topic // Research Trends, 2012, no.30, pp.3-6. 11. Raghavendra K. et al. The anatomy of big data computing // Software: practice and experience, 2016, no. 46, pp.79–105.

www.jpis.az 43 Problems of information society, 2016, №2, 34-45

12. Codd E. F. A Relational Model of Data for Large Shared Databanks // Communication ACM, 1970, vol.13, no.6, pp. 377-387. 13. DeWitt D., Gray J. Parallel database systems: the future of high performance database systems // Communication ACM, 1992, vol. 35, no.6, pp. 85–98. 14. Ghemawat S., Gobioff H. and Leung S.T. The Google File System / Proceedings of the Nineteenth ACM Symposium on Operating Systems Principles, New York, USA, October 2003, pp. 29–43. 15. Dean J., Ghemawat S. MapReduce: Simplified Data Processing on Large Clusters / Proceedings of the Sixth Symposium on Operating System Design and Implementation, volume 6 of OSDI ’04, Berkeley, CA, USA, 2004, pp.137–150. 16. Hadoop MapReduce, http://www.hadoop.apache.org/docs/stable/mapred_tutorial.html 17. Hadoop Distributed File System, http://www.hadoop.apache.org/docs 18. Diebold F. On the Origin(s) and Development of the Term "Big Data". Pier working paper archive, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, 2012, http://www.ssc.upenn.edu/~fdiebold/papers/paper112/Diebold_ Big_Data.pdf 19. Maier M. Towards a Big Data Reference Architecture, 2013, http://www.win.tue.nl/~gfletche/Maier_MSc_thesis.pdf 20. Imamverdiyev Y.N. Broad perspectives and problems of big data technologies // Information society problems, 2016, №1, pp. 23-34. 21. Clifford L. Big data: How do your data grow? // Nature, 2008, vol.455, pp. 28–29. 22. Kaisler S. et al. Money W. Big Data: issues and challenges moving forward / Proceedings of the 46th Hawaii International Conference on System Sciences, 2013, pp. 995–1004. 23. Tole A.A, et.all. Big Data challenges // Database Systems Journal, 2013, vol. 4, no. 3, pp. 31–40. 24. Laney D. 3D Data Management: Controlling Data Volume, Velocity and Variety. Technical report, META Group, Inc (now Gartner, Inc.), February 2001, http://www.blogs.gartner.com/doug-laney/files/2012/01 25. Alguliyev R.M., Ismayilova N.T. Bibliometric Analysis of Big Data Research / “Big data: capabilities, multidisciplinary problems and perspectives” First Republican scientific- practical conference proceedings, Baku, 25 February, 2016. Pp. 58-60. 26. Hey T., Tansley S., Tolle K. (Eds.), The Fourth Paradigm: Data-Intensive Scientific Discovery, Microsoft Corporation, 2009, 287 p. 27. Zhang Y., Zhao Y. Astronomy in the Big Data Era // Data Science Journal, 2015, vol. 14, no.11, pp.1-9. 28. Hays R. R., Daker-W. G. The care.data consensus? A qualitative analysis of opinions expressed on Twitter // BMC Public Health, 2015, vol. 15, no. 838, pp. 2-13. 29. Greene C.S., Tan J., Ung M., Moore J.H., and Cheng C. Big data bioinformatics // Journal of Cellular Physiology, 2014, vol.229, no.12, pp.1896–1900. 30. Wu Z. From Big Data to Data Science: A Multi-disciplinary Perspective // Big Data Research, 2014, vol. 1, p.1. 31. Jagadish H.V. Big Data and Science: Myths and Reality // Big Data Research, 2015, vol. 2, no 2, pp. 49–52. 32. Wu X., Zhu X., Wu G.Q., Ding W. Data mining with bigdata // IEEE Transactionson Knowledge and Data Engineering, 2014, vol.26, no. 1, pp. 97–107. 33. Alguliev R., Aliguliyev R., Hajirahimova M. Multi-document summarization model based on integer linear programming // Intelligent Control and Automation, 2010, vol.1, no.1, pp.105-111. 34. Omar Y. Al-J. et al. Efficient Machine Learning for Big Data: A Review // Big Data Research, 2015, vol. 2, no. 3, pp. 87–93.

44 www.jpis.az

Problems of information society, 2016, №2, 34-45

35. Gorodov E., Gubarev V. Analytical Rewiew of Data Visalization Methods in Application to Big Data // Journal of Electrical and Computer Engineering, 2013, 1-7 p. 36. Olshannikova E. et.all.Visualizing Big Data with augmented and virtual reality: challenges and research agenda//Journal of Big Data, 2015, vol. 2, pp.2-22. 37. Hajirahimova M. Sh., Aliyeva A.S., Review of statistical analysis methods of high- dimensional data // Eastern-European Journal of Enterprise Technologies, Kharkov, 2015, no 5, pp. 23-30. 38. Akerkar R. Big Data computing. Boca Raton, FL: CRC Press, Taylor&Francis Group, 2013, 562 p. 39. Sun Z., Pambel F., Wang F. Incorporating big data analytics into enterprise information systems, Lecture Notes in Computer Science, 2015, vol. 9357, pp. 300-309. 40. Kambatla K., Kollias G., Kumar V., Grama A. Trends big data analytics// Parallel and Distributed Computing, 2014, vol.74, no.7, pp. 2561-2573. 41. Chun-Wei Tsai, Chin-Feng Lai, Han-Chieh Chao and Athanasios V. Vasilakos, Big data analytics: a survey // Journal of Big Data, 2015, 2(21), 1-32. 42. Jiang J. Information extraction from text. In C. C. Aggarwal, & C. Zhai (Eds.), Mining text data (pp. 11–41). United States: Springer, 2012. 43. Kalampokis E., Tambouris E. and Tarabanis, K. Understanding the Predictive Power of Social Media // Internet Research, 2013, vol. 23, no. 5, pp. 544–559. 44. Barbier, G., & Liu, H. Data mining in social media. In C. C. Aggarwal (Ed.), Social network data analytics, United States: Springer, 2011. pp. 327–352. 45. Pekka P., Pakkala D. Reference Architecture and Classification of Technologies, Products and Services for Big Data Systems // Big Data Research, 2015, vol. 2, no 4, pp.166-186. 46. Klemenkov P.A., Kuznetsov S.D. Big data: contemporary approaches to storage and processing // The Institute of System programming materials RAS, vol. 23, 2012, pp. 143-156. 47. Alguliyev R., Imamverdiyev Y. Big Data: Big promises for information security / Proceedings of the IEEE 8th International Conference on Application of Information and Communication Technologies (AICT), Astana, Kazakhstan, 15-17 October, 2014, pp. 216–219. 48. Hajirahimova M. “Big data” technologies and the problems of information security // Information technologies problems, 2016, №1, pp. 49-56. 49. Lei X., Chunxiao J., Jian W., Jian Y., Yong R., Information Security in Big Data: Privacy and Data Mining // IEEE Access, 2014, vol.2, pp.1149–1176.

www.jpis.az 45 Problems of information society, 2016, №2, 46-56

Alovsat G. Aliyev1, Roza O. Shahverdiyeva2 1,2Institute of Information Technology of ANAS, Baku, Azerbaijan [email protected], [email protected] AN ANALYTICAL EXAMINATION OF INTERNATIONAL RECOMMENDATIONS ON STRATEGIC GUIDELINES AND PRIORITY AREAS FOR THE CREATION OF INNOVATIVE ENTERPRISES The article analyzes the need to create innovative structures in modern conditions, and justifies their role in the development of society. The principles, priorities and strategic requirements to the innovative structure in the process of economic development are analyzed. International, regional and local recommendations for the formation of innovative structures are generalized. Ways of improving the innovation infrastructure and environment are examined from the scientific and theoretical and methodological point of view. Priorities, mechanisms, elements of communication, the parameters and criteria for the management of innovative structures are identified. Keywords: innovative economy, innovative structures, Technology Park, business incubator, high-tech park, the future of enterprise. Introduction Advanced economy of the modern period is characterized with knowledge, new technologies and innovations. Thus, we can assume that high technologies will form the modern development basis of country’s economy. Currently, highest technologies that allow generating competitive products encompass all fields of developed countries’ economies, and provide the formation of economy established on the basis of generation, distribution and use of the new stage of information society – knowledge. Dynamics of scientific and innovative development is determined upon the organization process of transfer of knowledge and technologies. The elimination of underdevelopment in manufacturing and technology application fields of the country is one of targets in order to increase competitiveness of national enterprises and economy. The most important condition to be considered while developing development strategy of enterprises is the application of scientific novelties and science-based technologies [1]. The reason of economic development of Azerbaijan during the last decade is due to implementation of economic reforms supported by scientific basis. One of the most important duties is to accelerate the socioeconomic development using innovative and creative tools. Increasing intensiveness of innovative processes is one of the important directions of changes occurring in the global economic system. In developed countries, 75-90% of GDP growth and 10% of GDP growth in CIS countries is provided by the innovation sector [1]. CIS countries lose billions of resources as a result of recession in the innovation field. From this standpoint, it is important to achieve innovative economic growth for entry of the country into new foreign markets. In such conditions, serious attention must be paid to manufacturing and service structures, and the improvement of their innovative activity [2]. Relevant requirements and priorities have been created for the development of relevant entities. Strategic requirements and principals for innovative structures and systems have been indicated in accordance with determined duties. At the same time, possible control mechanisms’ development directions have been considered in order to regulate and stimulate innovation activities. For this reason, analytic and comparative analysis of international recommendations, instructions and agreements existing in that field is necessary, in order to improve the control mechanisms formed in order to stimulate, generalize and scientifically justify current and perspective requirement set towards innovation structures of different character, assignment and level, as well as their stimulation and regulation of their activity in integrated manner.

46 www.jpis.az

Problems of information society, 2016, №2, 46-56

Organizing innovation activity in international economic agencies and enterprises There are many international economic agencies worldwide that are engaged in problems of innovative enterprises, entrepreneurship and innovation technologies. The United Nations University – Maastricht Economic and Social Research Institute on Innovation and Technology (UNU-MERIT) has conducted researches in economic, innovation and innovative entrepreneurship fields. Role of innovations in economic development and factors of innovative environment have been researched in those researches [3]. United Nations Economic and Social Council (ECOSOC) serves as the central forum acting in innovation thinking development field and supporting continuous economic development [4]. ECOSOC has several regional branches in Africa, Asia, Europa, Latin America, Asia and Pacific Ocean basin. Event organized on “Innovation and technology for development of knowledge-based economy in Arab region” conducted in Amman, Jordan in 2015 by the technological development commission can be sited among regular actions taken by them. Topical issues, such as trade and development, information economy, e-commerce innovation and broadening of application of technologies, can be found on the official web site of United Nations Conference on Trade and Development (UNCTD). Besides, UNCTD continues its research in fields such as creative economy, industry, technologic innovations, e- commerce, investments, ICT, activities of innovative structures. Statistical indicators of UNCTD can be found on its web-portal. These indicators include 1) international trade, 2) economic growth,3) foreign investmnets, 4) information economy etc. [5]. Overall, currently, there are 194 member states of UN conference on Trade and Development. Azerbaijan is among top 20 member states. UNCTD has structural divisions such as 1) trade and development committee, 2) investment, entrepreneurship and development committee, 3) science and technology committee for development [5]. UNCTD has conducted many international conferences in different years. UN Special Programme is a joint effort of UN and Central Asian countries going through transition phase. Main objectives and activity directions of the programme were expressed in Tashkent Declaration in 1998. The objective of the program is to support and encourage integration of regional countries, attraction of internal and external resources to financing of priorities, more active application of standards and norms of Economic and Social Commission for Asia and Pacific and The United Nations Economic Commission for Europe (UNECE). Azerbaijan was accepted to the 4th session of Regional Consultation Commission conducted in Bishkek city in 2002 [6]. Another important agency - Organization for Democracy and Economic Development (GUAM), was created in 2006. The main objectives of the organization is the recognition of democratic value, providing the rule of law and human rights, sustainable development, deepening European integration in order to create a general security environment, also broadening of economic and humanity cooperation. Let’s remind that UN Development Programme (UNDP) has adopted “Knowledge control strategy in 2014-2017 years”. Moreover, following can be sited among important annual reports and events conducted by UN DP [7]: 1) Annual summary of innovative enterprises for the year of 2014 2) Discussion report on “Innovation monitoring and assessment” prepared and knowledge and innovation group and conducted in 2013, 3) “Knowledge and innovation report” for 2011-2013 years for Europe and CIS region [7]. International Association Of Organization Innovation (IAOI) was established in Florida, USA in 2005 [8]. IAOI is a professional organization specialized in management, development of innovative products, services and technologies and performance of engineering works. The mission of the organization is to increase the role of innovations and new technologies in the operations of the enterprises, create new enterprises by strengthening international relations, and

www.jpis.az 47 Problems of information society, 2016, №2, 46-56 achieve more efficient results in their operations. IAOI International Journal publishes over 300 articles on innovations [8]. IAOI conducted the 12th European conference on “Creativity and innovation” in Portugal. This organization also annually conducts international conferences in South-Asian countries. One of those conferences was held in Indonesia in 2015, and covered topics such as: 1) innovation, 2) innovation, knowledge and technologic management, 3) e-innovation, 4) innovation entrepreneurship, 5) globalization and economy, 6) information management and e- business [9]. The European Association for the Transfer of Technologies, Innovation and Industrial Information (TII) is located in Luxembourg. Its mission is to create a global society to support development of regional innovations and transfer of technologies [10]. TII held an annual report conference on “Innovation: Challenges, Needs and Skills of the New Innovation Era” with participation of 31 countries in Dusseldorf, Germany, in 2010. The head office of International Association of Scientific Innovation and Research (IASIR) is located in Georgia, USA, with regional offices in India, Canada, Australia, Germany and Netherlands [11]. IASIR is a non-commercial international innovative organization. The mission of IASIR is to accelerate development of science and scientific-innovation researches, create mutual relations in different fields of management and engineering, as well as among scientists and specialists, to conduct experience exchange and assist in development of innovation technologies. The objective of “Technopol-Moscow” Scientific-Technical Association (TMSTA) is to develop scientific-technical and intellectual potential. Its objective is to assist local and foreign companies in transfer of technologies [12]. Furthermore, there are agencies connecting and regulating the activities of innovation structures worldwide, in a certain form. The principal agency - International Association of Scientific Parks (IASP) was established in 1984. [13]. With its head office located in Spain, IASP has 396 members, 128000 companies, 73 member states and 6 regional divisions. Some other international organizations are also engaged in regulation of activities of innovative structures: 1) Association of University Research Parks, 2) Asian Science Parks Association, 3) The United Kingdom Science Park Association, 4) World Technopolis Association, 5) Russia Technopolis Association etc. Each organization conducts relevant works with innovation enterprises in accordance with its objectives. International organizations, such as World Alliance for Science and Technology Parks Association (WASTPA) [14], and National Association for Business Incubators (NABI), conduct analysis and accounting of technoparks around the world [15]. It must be noted that, in its turn, NABI is also a member of World Alliance for Science and Technology Parks Association (WASTPA). WASTPA is a global network uniting major science park and innovation-type business incubators. Recommendations of European Union Committee for Innovative Production Structures As noted above, under current conditions, specialized agencies of the UN, and several regional organizations provide their recommendations and proposals to enterprises and structures regarding their re-establishment and improvement of their activities. One of the most important recommendations on innovative structures belongs to the European Commission. Priority fields of cooperation among many countries within the European Union include important directions, such as diversification of economy, in order to reduce diffierences among territories, supporting development of regions to provide more balanced and comprehensive development, development of education systems meeting the working power needs in the labor market etc. One of such important directions is the concept of “Factories of Future” directed at identification of the future development directions and formation tendencies of enterprises,

48 www.jpis.az

Problems of information society, 2016, №2, 46-56 engaged in production sphere (European Factories of the Future Research Association (EFFRA) Factories of the future FOF) [16]. The essense of the concept adopted by the European Union Commission is to provide recommendations of future enterprise’s structure and activity. In this concept, manufacturing is indicated as the main condition for solution of major social problems. The importance of manufacturing potential of an enterprise is justified, and the directions for increasing the manufacturing capabilities of an enterprise are described.

Development directions of future ve innovative Increasing

Application manufactuing role of of enterprise human intellectual factor systems

Considering Determining Increasing Developing mobile development innovation and efficiency of future management perspectives of scientific-research enterprise’s activity structure of future directions of future products to be enterprise enterperise manufactured in the

future

Wide application of Innovation Wide use of Increasing Modernization of advanced role of highly capabilities of services the enterprise, directed at tecnologies skilled data base modelling, demands of and ICT specialist and development and strimulation and forecasting the customer scientists management

technology

Figure 1. Development directions of future innovative manufacturing enterprise[16] Specific directions were given priority in information map developed on the basis of the action plan of “Enterprise of future” (Figure 1) [16]. Several directions were included in the research priorities of enterprises of future, such as: 1) sustainable production (environmental safety, economic development, social safety), 2) capabilities of ICT in intelligent manufacturing (intellectual, virtual and digital enterprises), 3) high-quality manufacturing (adaptive equipment, accurate manufacturing, tools of planning and modelling of manufacturing, non-waste technologies), 4) employment of new materials in production (science- and technology-intensive, small-scale) and etc. [17]. Overall, the solution of all social problems in Europe hinges on various factors. Hence, great attention is devoted to state-private sector cooperation at the level of European Union Commission. The primary goal of “Europe- 2020” is the achievement of economic sustainability, the expansion of intellectual capacity and the comprehensive development [18-20]. For this purpose, the program of complex measure must be developed and carried out in various directions such as: 1) 75% of work force must be between 20-64 age years; 2) the increase of share of state and private sectors in investments devoted to regional development; 3) the achievement of favourable climate and energy changes; 4) the reduction of the level of environmental pollution; 5) the increase of energy volume obtained from renewable energy sources.

www.jpis.az 49 Problems of information society, 2016, №2, 46-56

Innovative trends in state and private sector cooperation The state and private sector cooperation must lead to the achievement of the same direction and same purpose of general and specific goals in the future. Untill 2030, large-scale tendencies in the production, under the cooperation of state and private enterprises, must be considered (Figure 2). Several long-term broad trends are envisioned in “Vision for the future – 2030”. Particularly, below-mentioned four long-term trends must be considered for the conduction of necessary integration of manufacturing process in Europe [21]: 1. The natural development of enterprise must be based on “green” and sustainable economy. The consumption of energy resources must be small-scale, clean and “green”. Resource scarcity in manufacturing must be eliminated, the sustainability principle of employees and material assets must be followed in manufacturing processes [16]. 2. Manufacturer must be in constant contact with consumer in the enterprise. The manufacturing must be organized in accordance with interests and demand of corresponding cities and the surrounding districts, as well as the consumers. An enterprise must be integrated into the living environment. The measures oriented to manufacturing must be oriented to the integration of customers.

Dynamism of innovation technologies (virtualization Global knowledge and ICT, technology society (know-how and Development of diffusion, development of new database, gender mass media life sciences, global inequality, struggle for communication, talents, security of digital technologies) information markets and etc.)

Innovative Resource (energy, water and other resources) trends Climate change scarcity problem (global affecting the warming, structure of environmental threat) production Exchange of globalization responsibilities (shift to global cooperation, Demographic changes strengthening of (growth of world manufacturing population, ageing society potential) and the growth of urban

population) Globalization of future markets

Figure 2. Innovative tendencies affecting the structure of manufacturing [16] 3. Enterprises must be chainwise linked. The main goal of manufacturing is the achievement of high level of competition (solid, flexible, rapid and changeable). European manufacturing system must consist of individual, design-oriented and mass-produced specific products. The integration of product and technological processes must be chainwise linked based on cooperation from simple to complex. 4. Brain centers must be established within enterprise. Employees must participate in the processes oriented to modelling and visualization. The role of ICT must be increased in product manufacturing, and the advancement of education level of workers with different degree of specialization. Work and wage system corresponding to decent living standards will lead to

50 www.jpis.az

Problems of information society, 2016, №2, 46-56 balanced regional develoment. The knowledge development will faciliate the high-level management of economic processes and financing. Those trends will substantially affect the structural changes in all fields of production. Opportunities and problems of enterprises of future In general, as a result of interest, attention and care, oriented towards the concept of future development of enterprises, “Enterprise of future” International day is annually celebrated on the 19th of May. Recently, scientific-theoretical analysis and discussion of the concept of future development of enterpises has taken place in the scientific conference held in Warsaw (Poland) on 19th of May, 2015, with the participation of representatives of 71 countries [22]. This conference was organized by the European Network of Enterprises, National Center of Research and Development, European Research Program and other well-known international and regional organizations and scientific-research bodies. Regarding the broad trends of the Concept of Vision of the Future-2030, it can be mentioned that European manufacturing sector must go through innovative changes till 2030. For this purpose, the following must be established in high-priority scientific-resarch and innovative sectors: 1) advanced manufacturing processes, 2) adaptive and smart manufacturing systems, 3) electron, virtual and resource-efficient enterprises, 4) joint and mobile enterprises, 5) human-brain centered manufacturing, 6) customer-oriented manufacturing. Alongside with various issues, the prospects of ICT application in manufacturing were discussed in the conference dedicated to the prospects of implementation of ICT [17]. It was specifically mentioned that main capabilities of ICT are various and as following for improvement of manufacturing systems at different levels: 1) Intellectual enterprises must be modified in accordance with the requests of flexible manufacturing and customers. The goal of this process is to achieve the automatization, better control and the optimization of processes within an enterprise. Main tools include the use of program software, laser, intelligent technologies and etc. in construction of infrastructure of enterprises. The productivity of enterprise is carried out in different directions in such situation: 1) less waste, 2) less energy consumption, 3) reduction in time of market access, 4) quality improvement and etc. 2) Virtual enterprises pay attention to the global network of manufacting and logistics, value generation, and the advancement of the efficiency of management of global manufacturing network. In order to provide the increase of product value, the efficient use of ICT, development of product and service systems, and the effective management of varying production situation must be achieved. One of the goals of this process is the management of procurement chains and the generation of new value by integrating goods and services. Process tools include the complex integration of program software, the management of distributed enterprise assets, as well as the development of new business models. 3) In digital enterprises, ICT is implemented during the design of larger production and management of product lifecycle, knowledge collection and management, the application of mutually conforming models for products and processes, design works, and the management of lifecycle. Primary tools include software, product testing, the processes till product manufacturing and utilization [23]. Regarding the production capabilities and problems of enterprises of future, it must be mentioned that the products of enterprise of future must be more competitive, environmentally clean and of high-quality. The production must possess economic, social and environmental sustainability for the manufacturing of such products. Main innovative manufacturing technologies and software systems must be developed and implemented in enterprises of future. Scientific-research and innovative priorities of an enterprise must be specified. Primary production processes of enterprise must be based on advanced tendencies and systems, and its effective organization on innovative scientific research.

www.jpis.az 51 Problems of information society, 2016, №2, 46-56

Management technologies of the performance of enterprises of future The priority must be given to the manufacuring of innovative products by means of development and application of specific control and monitoring systems, adaptive and intellectual systems during production processes. The following must be taken into consideration during the manufacturing of adaptive and smart manufacturing devices, system parts and equipment [24-26]: 1) technologies and robots with dynamically changing configuration, 2) stepwise formation of creative mind in support for the use of technologies and robots, 3) the maintenance of safety in technology and robot manufacturing and the enterprise of necessary cooperation for mutual links of production, 4) the use of smart technologies and robots in expansion of flexible manufacturing, 5) maintenance of new technologies with effective structure of adaptive and developing enterprises, 6) effective use of manufacturing machines with new structure and resources necessary for high-tech manufacturing, 7) application of nano-technologies in maintenance of precision of micro- and macromanufacturing equipment, 8) application of multidisciplinary technology in manufacturing and services. In order to achieve dynamic development in manufacturing enterprises, the automatization of enterprise manufacturing must be carried out. For this purpose, formation of dynamic, flexible and intelligent management systems is required for the management of manufacturing. At the same time, monitoring system must be established in manufacturing [16]. In order to solve the problems of existing problems of future enterprises with help of innovation and ICT, the following must be taken into consideration: 1) cooperation in design, technology and services sectors, 2) security issues of information exchange, 3) monitoring of visual tracking of resource flow, 4) dynamic modification of orders, 5) detection of risk potential, 6) elimination of asymmetric distribution of information managing the lifecycle of final products, 7) consideration of complexity of resource provision. The application of cloud technologies and ICT tools is inexcapable in enterprises of future. The rich practice existing in enterprises must be used, and the integration of real roduction resources must be carried out [27, 28]. Enterprises of future must also benefit from the most advanced technologies as digital, virtual and resource-saving, effective enterprises. It is necessary to employ intelligent systems for the advancement of reliability of manufacturing systems. A production cycle must be managed integratively in high-tech production enterprises. The monitoring and management of production, services and resources must be carried out in enterprises of future. Decisions must be made based on the analysis of high-level and competitive models for the improvement of product quality in enterprises. In some cases, production risks must be assessed and models of module type required for rapid adaptation of enterprises must be developed when necessary. Intellectualization of management and production processes of enterprises Development concept of enterprises takes into consideration the intellectualization of management, as well as production and services processes. Basically, automatization of production and services processes, and the application of intellectual system in that field, preconditions for the design management , intellectualization of planning, quality, services, diagnostics, procurement and etc. on intellectual grounds. Eventually, primary stages of management are carried out in intellectual form. Management of production processes based on new knowledge and technologies possesses specific features in accordance with the type of system and product, organizational structure, material reserves, information resources and systems. Several features such as technical and administrative management, processing of meterials, services, support, security, innovative environment, automatization, program administration, various scalability, configuration capability, modernity and adaptivity pertain to production system [23, 29].

52 www.jpis.az

Problems of information society, 2016, №2, 46-56

Assessment factors of human role in innovative enterprises of future

Application of e-learning methods in Intellectual production of specific manufacturing and education products

ICT-based solution of environmental Establishment of modern information production process of products model in science learning Specific purchase sources of innovative products Sustainable adaptation of the level of automatization to work places Sustainability of modelling of products and services Development of modern models in mutual relations of employees and Calculation and evaluation of value of technologies in product systems technological products

Application of comfortable interface Product development in manufacturing systems for employees in dynamic work center with Customer request conditions Compliance and assessment of quality of Modern technologies for monitoring and customer-oriented product analysis of human activity manufacturing with standards

Innovative management of creative Use of primary ICT tools for attraction of scientific potential in manufacturing young and old generation to production process

Effective distribution of financial assets

within enterpise Employment of intellectual enterprise and production technologies

Efficient user of resources and

Increase of customer -oriented service investments attracted to research and innovations and product manufacturing

Figure 3. Assessment factors of human role in innovativeenterprises of future

The application of automatization tools attracts broad attention in production systems particularly. Alongside with digital enterprises, digital products, engineering and management in digital environment, management of product lifecycle in global network environment, the application of intensive communication technologies in real-time period, intellectualization in production-procurement chain, ICT security and etc. problems are attempted to be resolved.

High energy efficiency and productivity can be achieved by means of achievement of ergonomical human-computer interfaces, flexible management of working time, the integrity of science, education and training with support of ICT, scientific modelling of the process, development of individual production solutions, the flexibility of marketing process and the application of distant production and intellectual technologies [17]. The development of strategic management system, management of technological manufacturing, enterprise and management of production system, development and management of enterprise’s strategy, and necessary integration of existing tools and methods must be

www.jpis.az 53 Problems of information society, 2016, №2, 46-56 implemented for comprehensive and mobile management of enterprise and resources. The following can be attributed to the main outcomes expected in automatization and intellectualization of production processes [17, 30]: 1) direct economic impact on innovations and resarch in production; 2) support for development of small and medium enterprises; 3) support for conduction of scientific results applied in several industrial sectors; 5) manufacturing of a market-competitive product; 6) enterprise of cooperation among scientific and industrial fields; 7) orientation towards European manufacturing, enterprise of tight links with regional clusters; 8) the increase in share of sales of Europen manufacturing equipment; 9) the achivement of environmental and economic priviledges due to the use of new technologies and etc. Several primary aspects must be considered for the management of the role of human and work place in innovative enterprises of future [16, 31]: 1) how people work and learn; 2) mutual relations of humans with technology; 3) human contribution to production value and etc. (Figure 3). The efficiency of innovative enterprises can be achieved through the acceleration of the formation of intellectual and creative human potential, the increase of effectiveness of the structure and dynamics of innovative personnel potential, advancement of professional staff training, intellectualization of the level of management of human resources, the provision of participation of experts in conduction of scientific-innovative research, and the increase of the participation level of human potential in innovative management of enteprise performance and decision-making by developing social-cultural and public activities of human resources. Conclusion While the economic development of a country in modern world is mostly achieved through innovations, one of the primary goals of the society and economy is considered to be the enterprise of innovative structure of new type which is the driving force of economic development. Corresponding strategic principles, goals, obligations, priorities and etc. must be indicated in national strategies and state programs determining the specific aspects of any country’s development directions in a specific situation. Alongside, several measures must be specified regarding the formation and implementation of action mechanisms of those structures. General strategic principles and priorities existing in international practice must be taken into consideration in this case. The goals of enterprise of innovative bodies and the base principles of their performance must be determined in these conceptual documents. Organizational, functional forms, features, as well as the main characteristics of those have been specified. In innovative bodies, the issues such as the implementation forms and types of innovations, innovation stages and periodicity are clarified. Corresponding measures must be carrried out regarding the development and implementation of modern management mechanisms and systems. As observed, the issues of development of diversification and modernization related to innovative structures, posed at international level from scientific-theoretical, technological and methodological point of view, facilitate the accurate specification of innovative criteria and parameters in management processes in those structures. This process creates new opportunities for the expansion of innovativeness, the selection of more favorable development options, and the efficient commercialization of scientific-research activities. The consideration of international and regional recommendations in new economic situation leads to the acceleration of integration processes and efficiency increase of the production-service processes.

54 www.jpis.az

Problems of information society, 2016, №2, 46-56

References 1. Musayev A.F. Innovation economy and tax stimulation. Baku. “Azerbaycan Universiteti” Press, 2014. 184 p. 2. Alguliyev R.M., Aliyev A.G., Shahverdiyeva R.O. The content of innovations and structural analysis of their features in the formation of information economy // Life Science Journal 2014,11(12), p. 119-125 3. United Nations University. Maastricht Economic and Social Research Institute on Innovation and Technology, http://www.merit.unu.edu 4. United Nations Economic and Social Council, https://www.un.org/ecosoc/en/ 5. United Nations Conference on Trade and Development, http://unctad.org/en/ 6. http://www.economy.gov.az 7. United Nations Development Program, http://www.undp.org/innovation2014. 8. International Association of Organizational Innovation, http://www.iaoiusa.org 9. http://www.ijoi-online.org/call-for-papers.html 10. Association for the Transfer of Technology, Innovation and Industrial Information, http://www.tii.org 11. International Association of Scientific Innovation and Research, http://www.iasir.net 12. Research and Technical Association "Technopol-Moscow" http://www.technopolmoscow.com/ru 13. International Association of Science Parks and Areas of Innovation, http://www.iasp.ws 14. World Alliance for Innovation. http://www.wainova.org 15. National Business Incubation Association (NBIA), https://www.inbia.org 16. European Commission. Factories of the future multi-annual roadmap for the contractual PPP under Horizon 2020. Prepared by: European Factories of the Future Research Association (EFFRA) a Manufuture İnitiative, http://www.ec.europe.eu European Technology Platform (ETP) for Future Manufacturing Technologies: ManuFuture, http://www.manufuture.org/manufacturing 17. Sibalija T. Intelligent Manufacturing: Challenges and Trends / Conference Factories of Future for Thailand, Bangkok, 15-16 January 2013, pp. 2-5. 18. European Commission Innovation Union – A pocket guide on a Europe 2020 initiative. Research and Innovation, Luxembourg, Publications Office of the European Union, 2013, 16 p. 19. Digital Agenda in the Europe 2020 strategy, https://www.ec.europa.eu/digital-agenda/en/digital-agenda-europe-2020-strategy 20. Enterprise Europe Network, http://www.effra.eu 21. Peltomaki A. Innovation in European Manufacturing. Manufuture 2013. View on Horizon 2020: sustainable re-industrialisation of Europe / European conference for Engineering industry and research, Vilnius, 6-8 october 2013, pp.37-39 22. International Networking Day on Factories of the Future and H2020 “Factories of the Future” Brokerage Event, Warsaw, 19 May 2015, http://www.fof2015.eu 23. Westkämper E. Digital and Smart Factories. Manufuture 2013. View on Horizon 2020: sustainable re-industrialisation of Europe / European conference for Engineering industry and research, Vilnius, 6-8 october 2013, pp. 115-118. 24. Musher S.L. Collaboration in Manufacturing through Collaboration in Innovation. Manufuture 2013. View on Horizon 2020: sustainable re-industrialisation of Europe / European conference for Engineering industry and research, Vilnius, 6-8 october 2013, pp. 43-46. 25. Herbert B.V. The role of Manufacturing for the European Economy / Manufuture 2011 conference. “West and East Europe in global High Added Value manufacturing – facts of today and challenges of tomorrow”, Wroclaw, 24-25 October 2011, pp. 7-9.

www.jpis.az 55 Problems of information society, 2016, №2, 46-56

26. Francesco J. European Innovation Partnership – New Industry / Manufuture 2011 conference”. West and East Europe in global High Added Value manufacturing – facts of today and challenges of tomorrow”, Wroclaw, 24t-25 October 2011, pp.31-32. 27. Zhang L., Jingeng M., Huntsinger R.C. Future Manufacturing Industry with Cloud Manufacturing / Cloud-Based Design and Manufacturing (CBDM), 2014, pp. 127-152. 28. Marenco C. Building an Excellent Science Base in Manufacturing. Manufuture 2013. View on Horizon 2020: sustainable re-industrialisation of Europe / European conference for Engineering industry and research, Vilnius, 6-8 october 2013, pp.31-33. 29. Kimura F. IT Support for Product and Process Development in Japan and Future Perspective. Digital Product and Process Development Systems // IFIP Advances in Information and Communication Technology, 2013, vol. 411, pp 11-23. 30. Aminuddin S.A., Nawawi M.K., Kalmoun E.M. Green manufacturing management: investigation of the philosophy practised in industry / 26th International Conference on CAD/CAM, Robotics and Factories of the Future, Kuala Lumpur, 26-28 July 2011, pp. 841-846. 31. Borisov V.N., Pochukaeva O.V. Modernizations of Russian manufacturing on the basis of the sustainable development of domestic machine building // Journal Studies on Russian Economic Development, vol. 22, issue 2, pp. 142-147.

56 www.jpis.az

Problems of information society, 2016, №2, 57-68

Zarifa G.Jabrayilova Institute of Information Technology of ANAS, Baku, Azerbaijan [email protected] FORMATION OF HUMAN RESOURCES FOR E-HEALTH: INTERNATIONAL EXPERIENCE, SOLUTIONS AND PERSPECTIVES The introduction and use of information and communication technologies (ICT) in health care, including e-health formation, and the realization of electronic medical cards, requires the appropriate human resources with necessary skills and knowledge. This article represents the topicality of the personnel shortage problem on IT specialties for health care - health informatics. The challenges on this problem solution in several countries, with succefully developed e-medicine, are analyzed. The programs and strategies for the solution of personnel education and training on the specialties of health informatics are analyzed. The development on personnel training in the integration of medicine and İCT in Azerbaijan are stated, and the recommendations are put forward. Key words: human resources in electronic health, health informatics, health informatician, health information manager, health cybernetics. Introduction The dynamic development and comprehensive application of ICT in all spheres of activity has led to the fundamental changes in these spheres. Consequently, all of these spheres of activity, their structures, forms and methods, development trends have undergone a serious transformation [1]. The formation of e-health is a good example. The development of ICT in the world has left the traditional services of health care system behind. Now, the medical information systems and EHR developed with the growth of ICT are used to support continuous medical assistance in separate medical institutions and regions [2]. These systems serve as the tools to support decision-making of geographically distributed users, doctors and the patients. They provide the accessibility of the knowledge base obtained from the clinical practice, and also increase the productivity and quality of the healthcare. Such systems enable everyone to get important information in the right place at the right time, which is very important for handling the situation by providing the necessary assistance in unpleasant situations. The countries with advanced E-healthcare mainly focus on the creation of suitable infrastructure (e-health) to provide better services to the enterprises, regions, and people connected to the health information system and network. Since the health information systems become common in the healthcare system and the traditional medical record keeping is replaced by the information systems and data analytics, the nature of the service provided to the patients is also changing. This is putting forward new demands on the performance of the highly qualified staff in the health care system, and at the same time, requires the integration of the experts in computer science into the health system. According to the experience of the national schools, which have achieved successful results in this field, the whole world has already realized that a key component of the integration of ICT in medicine is the human resources with necessary skills and knowledge. This need has been first reflected in the UK health programs [3], and then Australia and Canada began to focus on the development of the workforce of e-medicine [4, 5]. Health Information Technology for Economic and Clinical Health (HITECH) adopted in the US, which provides “constructive use” of the EHR system, emphasizes investing in the labor force [6]. Currently, it is of particular importance to develop human resource in e-medicine, and to design a data management support system, which collects medical data with high quality, and to train IT specialists to be in charge of the necessary technical equipment. Through e-Health, an infrastructure has been introduced as a tool connecting to the world; this infrastructure cannot be realized without the specialists in health informatics [7, 8]. Continuously generating health data and data sets cannot be managed,

www.jpis.az 57 Problems of information society, 2016, №2, 57-68 used and applied without the skills and professionalism required in this area. As the implementation of the national e-Health requires the specialists with the specific skills, they should also have ICT knowledge. Along with the deep understanding of the health care system, these professionals must have the knowledge in: - reengineering and project management; - health data collection, security and confidentiality; - human factor and technological processes; - technologies and supporting mechanisms providing access to the information systems [9]. The development of human resource strategy to meet the healthcare demands is challenging. On the other hand, along with IT knowledge, the medical IT professionals should also have knowledge in medicine, business and management. According to 88% of IT Directors of 91 institutions providing information services, they believe that the comprehension of health care system and the essence of the medical data is the major factor for the successful operation of IT practitioners working in the medical institutions [10]. Thus, this article explores the experience of the countries in training the specialists in the field of health informatics, in which successful results in e-Health formation have been attained. The article analyzes the attempts made in the “road map” of these achievements for the human resources development in medicine. Grounding on the international practice, the recommendations on the formation of human resources in e- health in Azerbaijan are put forward. e-Health human resource challenges The strategy for the training of human resources in e-Health is important for the health data collection, security and confidentiality, and for the high quality use of the system and data in the future [2]. To achieve the final results based on EHR, the employees must correctly use and manage the health data contained there. These employees are: - specialists in health informatics or health informaticians (HI); - health information managers (HIM); - specialists with ICT skills, and the knowledge in confidentiality and security of health records; human factor and technological processes; project management and technology application; experts capable to interoperate the user-server platforms and being aware of health care systems and data standards. The workers undertake the task to promote the health care system with the use of ICT. The development of human resources strategy for e-Health is very difficult. Because, the traditional supply and demand models used for the qualification and distribution of the doctors, nurses and other medical workers should include these innovations. To meet the demand for health informaticians, the UK and Australia adopted a national strategy, in 2002 and 2003, and later in 2009 [11-13]. The UK National Health Service allocated £ 12.4 billion for a 10-year national IT program to improve the safety and quality of medical care, and to realize e-Health and EHR system [14]. The program states that “the lack of qualified human resources is a serious obstacle to the realization of e-Health”, thus, there is a high demand for skilled medical workers, but supply is very low. The UK National Health Service proves that “the lack of workforce with the experience in IT applications and medical knowledge is a real obstacle to the realization of the medical project.” [15] notes that, in Canada, for the realization of e-Health resources, at least $ 7-10 billion are required, as the result of which 9000 health informaticians should be trained. The lack of IT resources is affecting the development pace of the projects realized in the field health care informatization in an entire country. Although financial support for the e-Health projects has

58 www.jpis.az

Problems of information society, 2016, №2, 57-68 increased, the qualified human resources have not been supplied, and the shortage of professional health informaticians still remains “a serious risk for achieving a success in EHR initiatives” [16, 17]. [16] states that Canadian Health Informatics Association, Canadian Association for Health Information Management, the Health Sector of the Canadian Association for Information Technologies, Canadian Infoway health structure and the Canadian Board of information and communications technologies started a joint research on defining the experts in national health informatics and health informatics management and on counting their number in e-Health. The US Altarum Institute offers 3-point evaluation in order to define the demand for human resources for the establishment of a national health information network within 5 years [18]: 1. complete introduction and operation of EHR system in hospitals; 2. complete introduction and operation of EHR system in hospitals and other medical facilities; 3. availability of medical IT professionals necessary for the creation of medical infrastructure, which provides the inclusion of health records from various sources in EHR and the access of each doctor and patient to EHR, more precisely, which ensures the relationship between all the systems. It is estimated that 400 000 physicians are required for practical use, and 7,600 more specialists - for the complete operation of EHR system, and 28 600 experts in about 4 000 hospital that need EHR system, and finally, 420 professionals are required for the installation of health information infrastructure [18]. These figures made the history, as the first quantitative indicators for the assessment of the demand for human resources to establish the national health information network. The databases of health information management service centers of the 5 000 American hospitals show that, currently, 40784 IT specialists are needed in order to get the complete EHR based on all registered electronic health data [7]. The survey conducted among the health information managers shows that, approximately, 53% of them were clinic or office doctors mainly employed in the inpatient and outpatient hospitals earlier, and about 19% of them were the employees of consulting firms [19]. At present, challenges related to the human resources in these countries are associated with the following problems [2]: - certification of the specialists in health information management (Canada and the US have already started this process); - certification of the specialists with necessary e-Health skills or regulating the licenses and providing legal base; - training and search of people with appropriate skills to become a part of necessary workforce in e-Health; - coordination of the strategy of e-Health staffing with the public health (healthcare) model. e-Health professionals and their competencies [20] defines e-Health as follows: “e-Health is the field developing at the intersection of health informatics, health care and business based on the transfer of health and information services over the Internet and related technologies. In a wider sense, the term characterizes not only the technical development, but also the level of cognition and thinking manner to improve the healthcare in the regions and all over the world through ICT. Thus, e-Health means computer applications, methods, tools applied in health system and the people performing all of these (suppliers, administrators, patients, families). For example, the studies and implementations conducted in the field of computer science are introduced to e-Health jointly by health

www.jpis.az 59 Problems of information society, 2016, №2, 57-68 information managers, researchers, technical experts and other field specialists [7, 9]. The professionals working in this field are called “e-Health experts”. e-Health professionals are using the concepts of computer science, methods and tools by bringing them together to support healthcare processes. e-Health professionals are referred to the health informaticians, health information managers, technical professionals and the specialists from various professions, who are capable to apply the concepts, methods and tools of computer science in e-Health to improve the effectiveness of healthcare system. Health informaticians - specialists with the competencies of health informatics in e-Health. Health informatics is a discipline which studies the research, development, design, implementation and evaluation of the data related to the concepts, methods and tools supporting the healthcare procedure and medical administration. [11, 12] define the term “health informatics”, and interpret the habits, skills and knowledge of these qualification owners. They are regarded as the e-Health professionals, and called “the owners of knowledge and skills providing collection, processing and access to the health data, which supports the provision of health services.” This definition also determines the future distribution of health informaticians. They are ICT experts (creators, rulers and supporters of ICT infrastructure - currently approximately 37%), EHR-creators, developers, and executors (data organizers, who match and choose the information about the patient - currently approximately 26%). [21] classifies the health informaticians as follows: - applied health informaticians are the professionals, who are deeply aware of the fundamental concepts of health. They apply the methods (including planning, management, analytics, procedures, etc.) to support the health processes and carrying out the installation of tools (e.g., information and communication systems). Their competencies are explained in [11] in details. One can master this profession with the Bachelor’s degree or certification in the field of medical informatics. - researcher-health informaticians design, develop and evaluate the concepts, methods and tools of health Informatics. The competence of these experts are provided in the section “exploration and development of health informatics” in the works [3, 4, 11]. The researcher-health informaticians should have the bachelor’s or scientific degree in the field of health information science. Those working at the educational institutions in the field of computer science usually tend to have scientific degree of PhD. The professionals of both fields of computer science can be clinical informaticians, and those who deal with telemedicine, health policy, health informatics visualization, and so on. Along with these two professionals, clinicians and administrators are also referred to health informaticians. Mastering advanced health informatics enough, they grow as professionals with clinical and administrative skills. Health information managers (HIM) – the specialist managing the health information of e- health team. The Canadian Association for Health Information directs the scope of the health information managers to the health data. The experts managing the health information operate both at micro and macro levels of clinical information management. At micro level (or personal health record level), health information managers are in charge of collection, use, accessibility, promotion, maintenance, support and destruction of health records regardless of their format. Health information managers perform high quality analysis of EHR documentation and are responsible for the safety of EHR use. Ensuring the security and confidentiality of health information, the managers should be interested in protecting human rights. At macro level (or aggregated data level), the managers realize the statistical analysis of the information contained in the health records. They perform integrated management of information systems by taking into account the interests of the state and individual parties for the improvement of the population’s health.

60 www.jpis.az

Problems of information society, 2016, №2, 57-68

American association for health information management evaluates the health data management as owning the knowledge and practice, which provides access to the HER to support decisions in real-time and critical situations. The managers can be presented as a clinical data specialist, the patient information coordinator, a manager of quality data organization, information security manager, information resource administrator, and a decision support expert. Technical experts - e-health team members with one or more technical qualifications. The systems they mastered may include operating systems, databases, programming languages, software, applied software (production facilities, administrative information systems, office systems, etc.), different equipment, communication and networking tools, biomedical engineering facilities, security, risk management methods, procedures, and so on. Other experts in health information technologies - refer to the professionals in ICT and business systems, human resource management, and industrial engineering. e-health specialist training Training programs of e-Health specialists, in the best case, are aimed at achieving the intended (projected) number of graduates that are needed to solve the demand problem in the field of e-health. Each year, these programs develop 100 specialists on health informatics and 200 specialist on health information manager in Canada. However, these short-term programs do not provide the demand for human resources in e-health. The short-term strategies also provide the training of the specialists and practitioners in the field of health information management and technical services. Health informatics school in Canada was formed in 1981. The University of Victoria supports the program at undergraduate and graduate levels. During the previous years, the bachelor’s program in health informatics was supported at the University of Dalhousie and Conestoga College, a master's program in health informatics was supported at the University of Dalhousie and the University of Toronto, and e-health is taught at the University of Mcmaster. Health information manager program is taught at the undergraduate level at the University of Ontario. The available programs offer the opportunity to expand the profile of the profession of health informatics specialists and to get the professional certificate; they also offer marketing, search for the jobs for specialists in e-health, computer science and computer technique [17]. Long-term strategy for human resources is intended to coordinate the training in the field of e- health at all levels (undergraduate and graduate). These programs are accredited by the Canadian Association for Health Information Management, are meant for long-term training of the specialists in health informatics and e-health. [6] states that 118 million USD was allocated for the training of specialists in e-health within the framework of HITECH program, which is shared among the following areas: 1) vocational training program in health informatics (70 million USD) - short-term certificate programs to train 10 000 experts in the colleges; 2) development centers program (10 million USD) – preparation of educational materials in local colleges and a national educational institutions and their distribution through the center; 3) examination program on competences (6 million USD) - testing the local college graduates on their competences; 4) university preparation assistance program (32 million USD) – developing the grants for the training of both undergraduate and graduate-level university students as the workforce with accordance to the certification programs. Canada and several other countries are going to make changes to the national occupational standards for the development of national health services. [22] provides more attention to this problem and emphasizes the development of professional standards for the qualifications in

www.jpis.az 61 Problems of information society, 2016, №2, 57-68 health informatics as an important issue and justifies the development of the unified code in health information technologies. The maintenance of health informaticians becomes challenging in national health care system. In general, there are some problems with satisfying the demand for health informaticians, EHR security, data protection and ICT staff on data development based. [12, 13] state that 43% of employees shifted to the similar positions in the private sector and 29% in the national health authorities. The reasons for this are low wages in the national sector, high demand, and difficult working conditions. However, mid-level health informaticians often have less problems. However, they also start to look for a better job as their knowledge and skills increase. Senior health informaticians are paid less than in the special sectors by 30-50%. Health informaticians often leave the workplace due to: 1) the lack of qualification and promotion; 2) the difficult working conditions (i.e., stress, unbalanced work regime, etc.); 3) and low wages. The formation of human resources ensuring successful e-health the following offers are made in [13]: • to be provided with a workforce in health informatics; • to develop the effective strategies for the recruitment to fulfill the functions of the position required by the employer; • to provide the information, which depicts professional portrait of the health informatician and defines his/her future career growth (EHR, information management, information services etc.); • to attract foreign workforce by providing incentive career growth (to attract those from other local medical clinics or the immigrants with necessary skills); • to open the vacancies on health informatics in the companies; • to develop lifelong education strategy; • to develop the skills appropriate to the stages of the career growth in order to attract a wide range of health informaticians; • to develop training programs (by different levels) on health informatics to ensure the sustainable development of knowledge, skills and experience for career growth; • to maintain (to motivate)the workforce on health informatics; • to ensure decent working environment, including fair wage structure; • to record and reward achievements; Australian national action plan mentions the importance of cooperating with the experts from all fields of health care system in order to define the demand for workforce on health informaticians [3, 11]. According to the Australian experience, it is important to define: first, who needs to study and what should be specifically learnt; second, which skills are vital. The action plan includes the following factors as the reasons for the lack of qualified specialists: - lack of appropriate educational programs; - lack of education funding; - the difficulty of attracting the students due to the unclear results of a health informaticians and health information managers, as they are new careers; - low wages; - lack of specialty status; - new skills required for health informaticians and health information managers, which are still not provided. - lack of coordination and management; - lack of a single program in the field of health informaticians and health information managers.

62 www.jpis.az

Problems of information society, 2016, №2, 57-68

Canada and the United States created the positions of the national coordinator and social service on the problems of healthcare information technologies in the healthcare structures of these states, in order to develop and implement a plan to improve the quality and efficiency of healthcare infrastructure with the use of information technology in 2008-2012 [22]. This plan promised to increase enrollments in health informatics in 2012 by 40%, and by more than 50% in 2014. American Health Information Association administration estimates 4 000 unoccupied position of health information manager due to the lack of enough workforce in the United States. It declares the shortage in this profession and stresses that the national health infrastructure will fail without the workforce capable to use and perform advanced technologies [23]. It also states that the increase in the quality and quantity of the workforce in health informatics is a critical component of the transformation of American health care system and ICT. In [7], it is mentioned that in 2014, for the realization of the desired model of the national health infrastructure and general HER system, at least, additional 41 000 experts in health information management are required. In advanced countries with forming E-health system, the national coordination centers and associations support the development of this field not only in their own countries, but also on undeveloped countries. American Association for Health Informatics received a grant of 1.2 million USD from Bill Gates and Melinda Foundation in December 2008 the development of workforce health informatics in the Latin America, Africa, the Middle East, Southeast Asia and the Pacific region countries [24]. The aim was to overcome the personnel shortage in health informatics in these countries, and to solve the problems of biomedicine and health informatics in education and training. Big Data analytics personnel problems of E-health Since 1980, stored digital information doubles every 40 months. Moreover, in recent years, it has been exponentially increasing. The generation of 2.5 Exabyte information a day (2.5 x1060 bytes) since 2012, has led to the formation of fast, large-scale and complex data massive at all levels of society, which proves the beginning of the “scary big data” age [25, 26]. Big Data (BD) is a set of data, which is beyond the capabilities of the traditional database processing, collection, storage, management and analysis tools. In this sense, huge data processing by means of conventional systems is hard. That is why they are analyzed and processed in order to obtain important information by establishing the correlation between these data. This also requires the training of the specialties with knowledge and skills on BD. Table 1 describes the skills of this personnel by their categories [27]. The US base SAS analytical company forecasts that the need for BD specialists will increase by 160% in 2013-2020, as a result, the number of jobs in BD will be increased by 346 000 units reaching 1 million. According to the recent studies, medicine is a field of science, in which extremely large amounts of data are accumulated, and 30% of collected and stored data throughout the world is health data [28]. Taking this into account, nearly 300 000 BD experts must be trained for medicine. Thus, the availability of the group of leaders and the personnel capable to how, when, where, and better way use the medical data is the demand of the day. In [27], it is stated that this staff earned £ 55,000 per year, which is more than the salaries of IT professionals by 2%.

www.jpis.az 63 Problems of information society, 2016, №2, 57-68

Table 1 Required staff qualifications on BD and their skills

Qualifications Required Skills 1. BD creator NoSQL, Java, JavaScript, MySQL and Linux together with TDD, CSS and Agile development knowledge 2. BD projectors Oracle, Java, SQL, Hadoop, and SQL Server and Data Modelling, ETL, Enterprise Architecture, Open Source and Analytics 3. BD analysts Oracle, SQL and Java together with Data Modelling, ETL, Analytics and Data Analysis 4. BD administrator Linux, MySQL, Puppet, Hadoop and Oracle along with Configuration Management, Disaster recovery, Clustering and ETL 5. BD project manager Oracle, Netezza, Business Objects and Hyperion together with ETL, and Agile Software Development – PRINCE2 6. BD designer Oracle, SQL, Netezza, SQL Server, Informatica, MySQL and Unix plus ETL, Data Modelling, Analytics, CSS, Unit Testing, Data Integration and Data Mining. 7. BD scientist Hadoop, Java, NoSQL and C++ along with Artificial Intelligence, Data Mining and Analytics

Training of personnel in e-health specialties in Russia Russia pays special attention to the personnel training for the formation of e-health. At many higher education institutions, the student enrollment in “medical cybernetics”, who will realize the transformation of medicine and ICT is increasing year after year. Medical Cybernetics – is a field of science dealing with the use of Cybernetics, ideas, methods and technical tools in medicine and health care. The training of the professionals in “Medical cybernetics” is carried out at the following educational institutions of the Russian Federation: -the Russian National Research Medical University after Piragov N.I. The training of this specialty started here for the first time in the department of medical biology; -Siberian State Medical University (Tomsk); -Penza State University; -North-eastern Federal University (Arkhangelsk); -Kazan (Privolzhsk) Federal University; -Pskov State University; -Krosnoyarsk State University after Prof. Voyno-Yasenetsk V.F.; -South-Western State University (Kursk); -Far Eastern Federal University (Vladivastok). At present, “health cybernetics” is a complete medical and its owner is a physician- cybernetics scientist. physician-cybernetics scientist can not work as a surgeon, therapist and other senior medical specialist (doctor-clinician) or enter residency. The graduates can work at clinical laboratory, functional diagnostics, radiology, and in the field of medical physics. Health Cybernetics can be presented in two groups:

64 www.jpis.az

Problems of information society, 2016, №2, 57-68

1. Mathematical diagnosis of diseases - is associated with the use of computer techniques in the data processing incoming from a biological object. In this case, the graduates of “health cybernetics” can work as the following physicians [29]: - clinical laboratory diagnostics physician; - X-ray doctor; - Physician-bacteriologist; - Physician-virologist; - Physician-geneticist; - Physician-mycologist; - Physician-radiologist; - Ultrasound diagnosis doctor; - Functional diagnostics doctor. The graduates of this specialty can work for the informatization of healthcare at medical institutions, health information-analytical centers, healthcare management centers, health insurance companies, including the companies developing and operating health information systems. They can also deal with the maintenance of medical diagnostic equipment. 2. Automated control systems and their capabilities in healthcare. The graduates of the specialty are able to achieve the knowledge in the following areas[30]: • Development, application and use of automated health information systems; • The use of computer techniques in the health data processing; • The use of modern clinical laboratory, bio-physiological and bio-chemical devices at the laboratories and departments of the medical and scientific organizations; • Verification of electronic-medical apparatus and basic troubleshooting; • Receiving therapeutic, surgical and neurological patients, defining their main symptoms and syndromes to set the preliminary diagnosis; • Drawing up a plan of laboratory and instrumental analysis; • Conducting the researches in clinical laboratory, biochemistry, bio-physics, immunology and medical genetics; • Appointing the diagnosis based on the results of the clinical, laboratory and instrumental studies, and selection of treatment tactics; • Organization and planning of the medical staff; • Organization of a variety of events related to the population’s health, healthy lifestyles, environmental health effects, and the prevention of diseases; • Providing emergency medical aid; • Delivering the laboratory and practical lessons in natural sciences, biomedical and clinical subjects at higher education institutions and colleges; • Developing scientific and methodological materials on the professional activity. The professionals in this specialty should be ready for the solution of the following issues [31]: - the development of public health planning and forecasting models with the use of mathematical methods and computational techniques; - the use of mathematical methods and computational techniques for the solution of the statistical data processing; - the development of the information support of the automated health care control system; - the development of the functional system model of an organism - the physiological system of the separate human organs, and their use for the diagnosis of the patient’s condition, automated control and forecasting with the use of information technology; - the use of applied software packages for the solution of computing diagnosis and the detection of informative indicators out of the clinical data massive; - the use of technology for the development of medical expert systems;

www.jpis.az 65 Problems of information society, 2016, №2, 57-68

- diagnosing the diseases by mastering the instrumental and laboratory research methods; - providing diagnosis and first aid in the emergency situations; - providing the medical aid to the population in the emergency situations as the epidemic spread, mass lesion and so on. The subjects taught in this specialty are: . computer science [32]; . information technologies in social sphere [33]; . computer science in psychology [34]; . health information systems; . clinical cybernetics; . clinical laboratory diagnostics; . medical biophysics; . medical electronics; . general and medical radiobiology; . system analysis and healthcare organization; . theoretical basis of cybernetics; . physiological cybernetics; . functional diagnostics. e-health staff training in Azerbaijan “Medical physics and informatics” department is operating (formerly called "Medical and Biological Physics” (adjunct to the course of computer science and computer engineering) at the Azerbaijan Medical University. The subjects “Medical and Biological Physics”, “Higher Mathematics” and “Computer science” are taught at this department. “Computer science” is taught at the 1st course of the bachelor degree and the courses on the office programs are taught one term at the master degree. Azerbaijan State University of Oil and Industry is training the staff on “Biomedical technology engineering”. The Students, majoring in biomedical technology engineering, are taking the subjects, as biology, computer science, biophysics, medical methods of diagnosing, computer technologies, physics, computer and engineering graphics, information technology, biomaterials and so on. These students are taught how to practice new biomedical devices, to introduce new developments in the production, to set up and operate biomedical equipment, to develop new biomedical devices and tools (computer tomography, blood pressure measuring devices, etc.), and to realize the certification and attestation of new biomedical technology. The graduates of “Biomedical Technology Engineering” can work as an engineer, laboratory engineer, medical equipment engineer, and environmental protection engineer at treatment and diagnostic centers, medical-biological centers, clinics and other relevant institutions. Conclusion Drawing upon the experience of developed countries, below recommendations need to be considered for the preparation of respective workforce and human resources, which is the principal guarantee of the formation of E-health in Azerbaijan: - demand for the personnel with the specialties appropriate to the E-health formation should be determined; - short and long term strategies and programs, which provide the training of required qualifications, should be adopted; - courses should be organized for the medical staff at different levels to develop their ICT knowledge and skills;

66 www.jpis.az

Problems of information society, 2016, №2, 57-68

- parties interested in the identification of the skills and competencies necessary for the e- Health workers: government, educational institutions, medical and IT experts, educators should be involved in this work; - new educational programs and professional standards in the field of health informatics should be developed; - qualifications should be recognized for the workforce recruitment, employee retention (motivation), and the stages of career growth shall be designed; - relevant associations and centers should be established for the coordination of experts. The realization of all above-mentioned items can be a successful step towards the integration of Azerbaijan into the international e-health environment. References 1. Alguliyev R.M., Mahmudov R.Sh. The transformation issues of the market economy in the information society //Information Society Problems, 2013, No2, pp.31-41. 2. Candace J.Gibson, H. Dominic Covvey. Clinical technologies. Chapter 5.1. Demystifying e- health human resources, 2011, pp.1403–1416, http://www.irma-international.org/chapter/demystifying-ehealth-human-resources/53656/). 3. Eardley T. NHS Informatics Workforce Survey. 2006, ASSIST: London, England,http://www.bcs.org/upload/pdf/finalreport_20061120102537.pdf 4. Legg M, Lovelock B. A Review of the Australian Health Informatics Workforce. 2009, Health Informatics Society of Australia: Melbourne, Australia, http://www.hisa.org.au/files/File/Australian_Health_Informatics_Workforce_Review_v1_ 1.pdf 5. O’Grady J. Health Informatics and Health Information Management: Human Resources Report. 2009, Prism Economics and Analysis: Toronto, Ontario, http://www.ictc- ctic.ca/uploadedFiles/Labour_Market_Intelligence/E-Health/HIHIM_report_E_web.pdf 6. Monegain B. Health IT effort to create thousands of new jobs, says Blumenthal, Healthcare IT News. October 6, 2009, http://www.healthcareitnews.com/news/health-it-effort-create- thousands-new-jobs-says-blumenthal 7. Hersh W., Wright, A. Characterizing the health information technology workforce: Analysis for the HIMSS Analytics database, 2008, www.http://www.medir.ohsu.edu/~hersh/hit-workforce-hersh.pdf 8. Ozbolt J. An environmental scan: educating the health informatics workforce in the global South / In Making the ehealth Connection Conference, Bellagio, Italy, 2008, July 13-August 8, 2008. New York: Rockefeller Press. 9. Covvey, H. D., Zitner, D., & Bernstein, R. M. Pointing the way: Competencies and curricula in health informatics, 2001, http://www.hi.uwaterloo.ca/hi/Resources.htm. 10. Monegain B. Healthcare IT: is it a breed apart? Healthcare IT News. September, 2004, http://www.healthcareitnews.com/story.cms?id=1522 11. Australia Department of Health and Aging. HealthConnect. Report on the health information workforce capacity think tank, 2003a, July 28, http://www.health.gov.au/internet/hconnect/- publishing.nsf/Content/7746B10691FA666CCA257128007B7EAF/$File/july03think.pdf 12. United Kingdom, National Health Services. Making Information Count: A Human Resources Strategy for Health Informatics Professionals, 2002, October, 40 p. 13. United Kingdom, National Health Services Connecting for Health: Professionalizing Health Informatics (PHI), 2009, http://www.connectingforhealth.nhs.uk/systemsandservices/capability/phi. 14. Coeira E. W. Lessons from the NHS national programme for IT // The Medical Journal of Australia, 2007, vol.186, no.1, pp.3–4. 15. Smith J. Wanted: Cyber clinicians to transform the nation’s healthcare system bit by bit // Canadian Healthcare Manager, 2005, June, pp.13–14.

www.jpis.az 67 Problems of information society, 2016, №2, 57-68

16. LaFleche C., Gardner N. Hitting the health informatics (HI) wall: A call to collaborative action on human resources // Healthcare Information Management & Communications Canada (HCIM&C), 2008, vol.22, no.3, pp.16–18. 17. Seaton B. A great time to be a Health Informatics Professional… (and not so great if you need to hire one) // Healthcare Information Management & Communications Canada (HCIM&C), 2008, vol.22, no.2, pp.16–17. 18. Department of Health and Human Services. Nationwide Health Information Network (NHIN) workforce study: Final report, 2007, September 19, http://www.aspe.hhs.gov/sp/reports/2007/NHIN/NHINReport.shtml. 19. Wing P., Langelier M., Continelli T., Armstrong D. Data for decisions: The HIM workforce and workplace 2002 member survey, 2003, Chicago: American Health Information Management Association (AHIMA), http://www.library.ahima.org/xpedio/groups/public/documents/ahima/bok1_018947.pdf 20. Eysenbach G. What is e-health? // Journal of Medical Internet Research, 2001, vol.3, no.2, e20, http://www.jmir.org/2001/2/e20 21. Hersh W. The Health Information Technology Workforce //ApplClin Inform., 2010, vol.1, no.2, pp.197–212. 22. Department of Health and Human Services. Office of the National Coordinator for Health Information Technology. The ONC-Coordinated Federal Health IT Strategic Plan: 2008- 2012, http://www.hhs.gov/healthit 23. American Health Information Management Association and American Medical Informatics Association. Building the work force for health information transformation. AHIMA: Chicago. 2006, Bethesda, http://www.amia.org 24. American Medical Informatics Association. Press Release- AMIA receives grant from Bill & Melinda Gates Foundation to develop a global biomedical and health informatics fellowship program, 2008 December, http://www.amia.org/files/Gates_GlobalFellowshipProgramPR.pdf 25. Big Data in Human Resource Management – Developing Research Context,file:///C:/Users/HP/Downloads/Big%20Data%20in%20Human%20Resource%20M anagement%20.pdf 26. Mammadova M.H., Jabrayilova Z.G. Big Data opportunities and problems in the solution of human resource management issues // Information Technologies Problems, 2016, No1, pp. 39–48. 27. SAS report on “Big Data Analytics Assessment of Demand for Labour and Skills 2013– 2020, 2014, http://www.sas.com/en_us/home.html 28. Manchini M. Exploiting Big Data for improving healthcare servuces// Journal of e-Learning and Knowledge Society, 2014, v.10, n.2, pp.23-33. 29. Health Cybernetics at Pskov State University, questions and answers, http://www.pskgu.ru/page/6ACD44BF4D7BBA8C8E8483979E0BBE9C 30. http://www.cmci.rsmu.ru/ 31. http://www.ee.swsu.ru/spec.php?SPEC_SHIFR=%CC%CA 32. http://www.cmci.rsmu.ru/fileadmin/rsmu/img/mbf/cmci/rab_programmi/up_information science.pdf 33. http://www.cmci.rsmu.ru/fileadmin/rsmu/img/mbf/cmci/rab_programmi/up_it.pdf 34. http://www.cmci.rsmu.ru/fileadmin/rsmu/img/mbf/cmci/rab_programmi/up_i_i_evm.pdf

68 www.jpis.az

Problems of information society, 2016, №2, 69-73

Afruz M.Gurbanova Institute of Information Technology of ANAS, Baku, Azerbaijan [email protected] DEVELOPMENT OF TERMINOLOGICAL INFORMATION SYSTEM IN THE SUBJECT FIELD The article provides an architecture and conceptual scheme of decision support information system for a wide range of experts of a certain subject in the field of terminological activity. It defines the opportunities created by the special knowledge database, which is oriented at term analysis of terminological information system. Keywords: terminological dictionary, information systems, ontology, semantic network. Introduction In modern times, one of the methods to increase the level of training of highly qualified personnel in science and education system is the development and use of knowledge and data mining systems in various subject areas. In recent years, significant changes and developments occurred in various fields of science, and technology has led to the enrichment of the terminological databases of separate subject areas and to the generation of new terms in the language. The growth of research and education centers in the world and Azerbaijan, the development dynamics of various fields of science and technology, meeting the international standards and ever-increasing demands for the terms, and other factors require the development and use of the terminological information system, which will provide the structuring of terms in a variety of subject areas, their concentration, processing and application. Problem statement The development of the ontology of separate subject fields serves to the training of the specialists of these fields both in the educational process and practical work. It also increases their level of knowledge. The terminology database of the given subject field can be presented as a system aimed at certain goals [1]. S = (1) M - denotes the set of system elements: terms in the subject field and their definitions, R - the set of relationship among the terms in the majority, and P - the set of system properties to reach the goal. As the elements of the terminology system, the terms should have the following properties: . To be free of subjectivity of the life experience. Imagining the various objects about the same term is unacceptable. . To be definite. That is the same term should not describe various objects of the same branch of science in different cases. . To have fixed values, in other words, the set of fixed objects, which are described by this term. The main feature of the system elements is that there is an exact definition of each term. To understand the term, it should have its definition, and the definition of the terms is to be used in its explanation. The relationship among the elements of the terminology system reflects the hierarchy of the concepts, interacting with each other. The relationship among the terms is determined by the linguists and experts of the subject field, likewise the terms. The nature of the established relations may differ.

www.jpis.az 69 Problems of information society, 2016, №2, 69-73

It should be noted that the terminological system is public: on the one hand, the system elements transform into the word in general use - determination, on the other, new elements – term flow (entry), are always included into the system. Each separate property (p1, p2, p3, ... pn) of the set P characterizes the local functional quality (for example, p1 - integrity, p2 - publicity, p3 - accuracy, etc.). Altogether, these properties characterize the system as a whole. The information system, here, is reviewed as software and hardware systems, and as the realized data bank (knowledge bank). The system automatically and unequivocally supports the collection, search, identification, acquisition, storage, preservation, processing, and transmission of data in accordance with the users’ requests within the framework of the certain methods, methodical and normative documents (standards). The development of the architecture of terminological information system The system architecture implies the information environment that covers the interaction of all its components, functions, structure, model, including the system itself. It is set on three levels (Figure 1).

“User-system” interface

Data processing

Knowledge base

Figure 1. The architecture of information system The first structural units of the system are the information objects, namely, the articles of the dictionaries. Here, a part of the object, for example, the title of the article, can be physically stored in the low level database (file). The text or a fragment of the article can be located or found in another database (file). However, the article is given to the request of the external interface, with the title, as a whole and in its original form. The 3rd level of the system architecture is performed as follows: . conceptual model of the knowledge base of the information system is created on the basis of the presentation of (1). . ontology of the subject fields is established in the form of hierarchy of common notions (M – denotes the set of terms and definitions), in order to provide the knowledge space. Ontology of the subject field – implies a formal description of the subject field. It is usually applied to identify the common terminology database of the subject field. In information technology and computer science, ontology means the set of the objects and the relations among their descriptions. Formally, ontology includes the terms, their description and access rules. Thus, the ontology is the model of the subject field, and accordingly, it is the basis for the knowledge bank and knowledge base. The main purpose of the ontology is to standardize the knowledge model and to maximally (mathematically) describe it [2]. The advantages of establishing the ontology are:

70 www.jpis.az

Problems of information society, 2016, №2, 69-73

. To facilitate the mutual access to the data structure about the subject field; . To facilitate the compliance of various parts of the subject field models; . To provide the availability and replacement of the models for new users; . To separate the knowledge structure of the subject field from the specific data. Ontology is developed in several phases. After defining the subject field, the search and reuse of existing ontology can be reviewed. Later, a complete list of the terms of the subject should be acquired, and the hierarchy of the classes should be developed and their features should be described. The following step includes describing the relationship between the ontology elements. At this phase, it is determined at which stage of the hierarchy the notion is, whether the object is classified or not, and so on. Separate examples of the classes are created at the last phase. The ontological approach to the modeling of the subject field provides the development of new information systems and the interoperability of the preliminary information systems. At present, a paradigm of two-level information systems is growing. In computer science, ontology is a detailed description of the certain knowledge fields with a conceptual scheme (semantic network of mutual relations of the notions and concepts within certain rules). Actually, this scheme reflects the structure of the data. It includes all relevant classes of the object, the links between them and adopted rules in this field (theorems, limitations) [3]. In the process of programming, ontology is used as a form of presentation of knowledge about the real world and its parts. It is mainly applied in the business process modeling, semantic web and artificial intelligence. Description language on the ontology is a formal language, which is used for its coding. Several similar formal languages (OWL - Web Ontology Language, KIF - Knowledge Interchange Format) are available. The analysis of different models for the data description has justified the selection of multi- component semantic networks as the tool for ontology description. The semantic network is an oriented graphics. Its tops denote the notions, and its edges show the relationships between the notions. The notion expresses any abstract or certain objects, whereas the relationship – the links between the objects. Semantic Web relations can be “partial-full” type (class-group, element-set), functional (“occur”, “affect” and so on.), quantitative (more, less, equal, etc.), spatial (far, near, under, above, in, etc.), logical (and, or, not), linguistic and so on [4]. In the knowledge base of a semantic network type, the search of solution is reduced to the search of network fragment, which reflects the query sent to the database in accordance with any sub-network. This model was proposed by an American psychologist Quillian M. [5]. The semantic network is implemented with the establishment of the thesaurus (an active tool for the description of the separate subject fields), where the hierarchy of the definitions is reflected. Unlike the explanatory dictionary, the thesaurus provides the notion not only by its definition, but also its understanding through the coordination with other words and groups. The thesaurus defines five types of relations: a broader term - top; more narrow term - bottom; associated (linked) term - association; whole for the term - part; part for the term - whole. It should be noted that, in fact, establishing the ontology in knowledge field and professional field of activity has a tendency to grow from field standard level up to technical materials. The establishment of the 2nd level of the system architecture is based on the model of text type data search. This model refers to the presentation of documents and requests, the criteria for logical compliance, the mechanism for the survey results ranking (by its importance), and feedback mechanism for the evaluation of the document relevance (survey compliance rate).

www.jpis.az 71 Problems of information society, 2016, №2, 69-73

The implementation of this model is associated with the search for the full text out of all documents. The user request rate (relevancy), search indexes relevance, reliability, service flexibility is determined by the search efficiency criteria. The first level of the system architecture is established using HTML programming language through the data availability, which is based on hypertext information presentation. It can be noted that the technical requirements of the software implementation are the ease of use of the information system, design compatibility, integrity, publicity, the presence of search system, the presence of query system, the availability of data protection tools, access and review of legislative documents and materials (the books related to terminology, terminological dictionaries, etc.). The system has been established in accordance with the standards of the International Organization for Standardization [6, 7]. The conceptual model of terminological information system on a given subject is presented in Figure 2. Thus, the development of the terminological information system has created great opportunities for the development of the specialized knowledge base aimed at term analysis and for conducting studies in the field of terminology in Azerbaijan [8].

Data update system

Data search block

Data Base

Search engine Data description block

interface

Data search engine Text data description system Legislative documents

Graphic data description system

User interface

Query system User authentication system Figure 2. Conceptual model of the information system Conclusion The development of such a decision support system and software for a wide range of specialists in terminology in Azerbaijan is the first step. From above mentioned, we can conclude that the terminological information system is a semantic network used on the set of terms. It is implemented through the thematic Internet portal

72 www.jpis.az

Problems of information society, 2016, №2, 69-73 that provides various interactive services to the users, where the key point is the search engine of the portal. It should be noted that certain implementations have been done on the software of the terminological information system. The software has been developed with PHP program language running under the Linux operating system. The term sought in any subject area, definition of the term, its translation into the possible languages and other metadata are reflected here.

This work is supported by the Science Development Fund under the President of the Republic of Azerbaijan - Grant No SDF-2014-9 (24) KETPL-14/02/1 References 1. Novozhilova M.V., Usherov-Marshak A.V., Latorets E.V., Mikheev I.A. Development of terminological information systems in the field of concrete study. Kharkiv State Technical University of Construction and Architecture, Thematic collection of “Data processing systems”, 2010, edition 6 (87), pp. 139-142. 2. Konstantinova N.S., Mitrofanova O.A., Ontology as a knowledge storage system, Saint Petersburg State University, St. Petersburg, http://www.ict.edu.ru/ft/005706/68352e2-st08. pdf 3. https://ru.wikipedia.org/wiki/Ontology (information science) 4. https://ru.wikipedia.org/wiki/The semantic web 5. Quillian M.R. Semantic memory. Semantic information processing, MIT Press; reprinted in Collins & Smith (eds.), Readings in Cognitive Science, section 2.1, 1968, pp. 227-270. 6. ISO / TR 12618: 1994. Automated terminological data processing tools. Creation and use of terminological databases and text collections, http://www.iso.org 7. ISO 12207. “The processes of software life cycle”, http://www.klubok.net/pageid313.html 8. Aliguliyev R.M., Gurbanova A.M. Conceptual framework of developing the terminological information system. Problems of Information Society, No1, Baku, 2011, pp. 3-8.

www.jpis.az 73 Problems of information society, 2016, №2, 74-81

Ramiz H. Shikhaliyev Institute of Information Technology of ANAS, Baku, Azerbaijan [email protected] SECURITY ISSUES IN SOCIAL NETWORKS Nowadays, a large number of social networks exist in the Internet. These social networks are very popular and play a prominent role in people’s life. Alongside, the social networks have also caused the occurrence of new threats in the field of information security. Such threats are related to the distribution of malicious software and spams, attacks on social engineering and social network accounts, tracking, fraud and etc. This article is dedicated to the analysis of existing threats in social networks and the protection issues against them. Key words: social network, malicious software, spam, phishing, fake account. Introduction Internet has become a main tool of global communication and information exchange among people. The establishment and rapid development of Web 2.0 technology has substantially broadened the capabilities of Internet and facilitated the access of people to social networks regardless their geographic location [1]. In turn, the rapid development and broad use of social networks has turned it into one of the main elements of Web 2.0 technology. Social network is a service which facilitates the establishment of connections and information exchange among people. At present, several social networks, such as Facebook, Twitter, Linkedin and etc. exist. These social networks become popular day by day, and play an important role in the society’s life. Depending on user interests, various specialized social groups – for example, business-oriented networks, such as LinkedIn and Xing, are created, which enable the users to establish business relations and to propose job opportunities. Some social networks are only oriented to the establishment of communication among people and act as an environment for virtual encounters. However, social networks bring out new problems related to the immunity of private lives of users and the information security. That is, the creation of social networks has led to the increase of security risks. These risks are related to the problems of different aspects such as the expansion of malicious software and spams, the attacks on social engineering and social network accounts, as well as tracking, fraud, blackmailing, smearing and etc. Alongside with indicated threats, social networks can also incur various threats to national security depending on the interests of users [2]. On the other hand, the number of social network users have rapidly increased and exceeded 2 billion people in recent times. According to forecasts, the number of social network users will reach 2,5 billion people in 2018 [3]. Such popular use of social networks and generation of a large volume of information by users has turned them into a target for attacks by malignant persons and offenders. Social networks are used by malignant persons as a favorable platform for conducting various kinds of attacks from spamming [4] till individual phishing [5] attacks. Naturally, the maintenance of information security and secure use of social networks in Internet environment have become a topical issue in such situation. Hence, the analysis of information security, social-aspect threats and the protection methods from those in social networks are of great importance. Such analysis assumes large importance in terms of the maintenance of information security and the secure use of social networks by people in the Internet environment. Social network threats The security in social networks mainly covers the issues of protection of users’ personal information from malignant acts. For this purpose, social network users must be aware of the risks and threats related to their personal information.

74 www.jpis.az

Problems of information society, 2016, №2, 74-81

The threats that can occur in social networks may be divided into four groups. The first group includes the conventional threats, especially the threats related to the immunity and security of private life. These threats cause danger not only for social network users, but also for other Internet users who do not use social networks. The second group captures the modern threats related to the immunity and security of private lives. These threats mainly pertain to social networks, and cause a danger for immunity and safety of private lives by using the social network infrastructure. The third group covers the combination of various threats, that is, more sophisticated and dangerous attacks can be conducted as a result of combination of various threats. The fourth group contains the threats with social aspects. Tracking, fraud, blackmailing, smearing and etc. can be attributed to such threats. Conventional threats remain as a problem since the beginning of the broad use of the Internet. Malicious software [6], spams [7], phishing [8] and etc. can be attributed to such threats. Those can be very dangerous depending on the structure and the character of social networks and may be spread to several user computers in short time. Such threats can cause a danger to users, as well as their “friends” by using the personal information of user posted in social networks. For instance, by using the details of users in Facebook profiles, malefactors may generate spam-information, which can be attractive at first sight and locate a malicious software code into such information. Considering that, such information is of personal character, it can be surely said that some user will open it and his/her computer will be infected with malicious software. In the majority of cases, the target of such threats is the daily and important user resources. These resources include credit card credentials, account passwords, computing power, impact zone and etc. Additionally, those threats may use the obtained information for forwarding information on behalf of the user of infected computer and even change the user’s personal data. The aim of creating malicious software is to collect the registration data of users and disrupt the performance of computer in order to gain access to individual information. In order to distribute such programs among users and their “friends” in social network, the structural features of social networks are applied. In some cases, malicious software programs use the registration data of users in order to send infected information to their “friends”. First malicious software spread in social networks such as Facebook, MySpace and Twitter was Koobface worm. While infecting, Koobface worm attempts to capture the registration data of users and to connect infected computers to botnet network [9]. Computers connected to botnet become “zombies” and thereafter, are used for malignant purposes such as spamming, attacks on other computers and services connected to Internet and etc. Phishing attacks are attributed to social engineering attacks, and used in order to obtain confidential information and personal credentials of users. For this purpose, an attacker acts as a reliable third party. Usually, social network users are exposed to phishing attacks due to their own sociality and naivety [10]. Hence, the attempts of phishing attacks have increased in social networks in recent periods. According to the Microsoft report on security [11], the target of 84,5% of phishing attacks, occurred in the Internet, were social network users. Spams are unwanted information of advertising type sent by the user called “spammer” to other users by using electronic information exchange systems. Social network spammers use the platform of social networks for spreading spams. For this purpose, spammers create fake profiles in a social network [12]. Additionally, spammers may use the social network platform in order to add the information of comment type in pages, while a large number of users review these pages. The modern social network threat pertains solely to this environment. Usually, the target of such threats is the personal data of social network users, as well as their “friends”. For example, malignant persons create a fake “friend” profile in order to attack the personal information of the Facebook user and send requests. If the target users accept the request of this “friend”, his/her personal data is exposed to a threat and malefactors are able to capture them. Additionally,

www.jpis.az 75 Problems of information society, 2016, №2, 74-81 malicious persons are able to extract some information regarding the “friends” of the Facebook user by compiling and analyzing the information pertaining to those. Existing modern social network threats are carried out according to various scenarios. For example, an attack called ClickJacking deceives users by inducing to click at first sight useful, but malicious links. By using ClickJacking, malignant persons may spread spams via “likes” by manipulating the users (this is also called likejacking) [13]. As an example to ClickJacking attack, “Don’t click” attack may be shown which occurred in Twitter in 2009. Violators have located masked (actual URL was hidden) URL address (a locator showing the address and location of a file or resource in Web) with “don’t click” information in Twitter, and as Twitter users entered this link, the information was spread as a virus and located in user accounts [14]. The majority of users in social networks use pseudonyms in order to maintain the privacy and anonymity. Malignant persons use the attacks called “de-anonymization” against them. During this attack, wrongdoers use malicious cookies (it is the technology storing and entering the information in user devices such as computers, tablets or mobile phones), the methods of tracking of network topology and user groups. Alongside, it is possible to identify them by the analysis of the information leak from social network websites [15]. Another method of “de- anonymization” is solely constituted of the analysis of the social network user membership in groups [16]. This method is tested in Xing social network, and as a result, 42% of users were identified. Another method is based on the comparison of user profiles of various social networks [17]. Usually, social network users post the photos of themselves and their “friends”. For example, millions of photos are posted daily in Facebook social network [18]. Additionally, the viewing and opening of photos of the majority of Facebook users’ profiles is public. For example, Faces of Facebook website [19] allows to view the profile pictures of more than 1,2 billions of Internet users. These photos can be used for the creation of biometric database and the identification of those social network users without their consent. Fake profiles (also called as “social bots”) imitate the human behavior in social networks as automatic or semi-automatic profiles. Mostly, such fake profiles are used for collecting personal data of social network users. For this purpose, social bots generate “friend” requests for social network users, and they accept these requests in most cases. As a result, social bots obtain an opportunity to gain access to personal data of users while the personal data of social network users is usually accessible for his “friends” only. Alongside, fake profiles can be used for conducting Sybil attacks [20], the spread of social spams [21] and etc. An attacker uses the reputation points of a person in order to manipulate by creating several identificators (Sybil). Nowadays, malignant persons may conduct more sophisticated and dangerous attacks by combining traditional and modern threats. For example, they can purposefully collect the passwords of Facebook users by phishing and post the information possessing ClickJacking attack. Hence, the malefactors become capable to induce “friends” of users to disclose the information by deceiving them and locate viruses in their computers. Several threats of social character exist in social networks and tracking, fraud, blackmailing, smearing and etc. pertain to those. Unfortunately, some people use the social networks in order to conduct such threats against other people. For this purpose, malignant persons benefit from personal data of social network users and various attack tools. In some cases, such threats are even used against different countries, organizations and etc., and the number of social threats has increased. Tracking in social networks is one of the most widespread and well-known threats of social character. While conducting such threats, malignant persons may obtain the personal data (location, phone number, work schedule, home address and etc.) and their profiles. They may impact the targeted users in different ways. For example, this impact may start with frightening

76 www.jpis.az

Problems of information society, 2016, №2, 74-81 people and evolve to blackmailing, privacy violation and even serious physical damage (for example, terror), psychological shocks and etc. Protection methods against threats in social networks Recently, various solutions have been proposed for the protection against threats in social networks. Such solutions capture different levels, that is, social network operators, security companies and the proposals of scientific researchers. Social network operators carry out various security measures for maintaining users’ safety. For instance, authentication mechanisms and several measures such as the regulation of personal data are applied. The mechanisms of authentication are applied to ensure that social network users are neither the social bots nor notorious user accounts, but real persons. That is, the authentication enables to ensure that real persons have registered in and entered social networks. For this purpose, various authentication mechanisms such as CAPTCHA (Completely Automated Public Turing Test to Tell Computers and Humans Apart) [22], the identification of an image of “friends”, that is the recognition of a “friend” among the images presented to suspicious user; multifactor authentication [24], that is, the entrance of additional information by users alongside with password and etc. are applied. Such authentication mechanisms enable to prevent the capture of personal data of users by malignant persons via social bots and notorious user accounts and the spread of malicious software and spams. The majority of social networks facilitate the users to control their personal data. This enables the users to protect their personal data from other users [25, 26]. For example, Facebook users can control their personal information and manage the permission to view those, i.e he or she can specify the user groups (“friends”, ‘friends of friends” and “everyone”) who can view other personal information [27]. Some social network operators also enable the users to carry out additional security configurations. These configurations allow the users to activate the secure view by users of their own personal data, to receive notifications about the access to their account and set other security features [28]. Notwithstanding this, most users are not able to manage the parameters of privacy settings of personal data and expose their personal data to threats [29]. Some social network operators apply additional internal security mechanisms in order to maintain the user privacy. Such safety mechanisms allow to be safe from the spread of spams, fake profiles, fraudulency and etc. threats [30]. For example, in order to prevent malicious attacks and unauthorized data collection, FIS (Facebook Immune System) is applied [31]. FIS carries out the analysis and classification of reading and writing operations in Facebook database, in real time regime. Social network operators have added an option of information sharing in social websites in order to protect the users of certain groups, mainly children and teenagers from being followed by other users [34]. In some countries, for example, they have added “Panic Button” to Facebook in order to protect children in social networks. Alongside, some social websites cooperate with certain organizations in order to protect potential victims (for example, children). For instance, Facebook has added a button for reporting regarding the suspicious behavior or abuse based on the request of the Organization of Children Protection of Great Britain in 2010. Several commercial solutions have been proposed by well-known companies in security sector for the protection against threats in social networks. For example, AVG, Avira, Kaspersky, Norton, Panda, McAfee, Symantec and etc. security companies have presented various Internet- security software to social network users. Usually, such solutions include antiviruses, internetwork screens and other Internet-security programs. Corresponding measure allows the social network users to protect their computers against the attacks such as malicious software, botnets, ClickJacking, phishing and etc. type. For example, AVG PrivacyFix software tool as a

www.jpis.az 77 Problems of information society, 2016, №2, 74-81 mobile application and web-browser add-in [34] allows Facebook, LinkedIn and Google users to control their personal data. Norton Safe Web [35] software as Facebook application searches for new “friends” of users and informs them regarding the malicious links and websites. McAfee Social Protection [36] software as a mobile application enables the users to protect the images posted in Facebook pages by users, to view and download those images by other users. Various solutions presented by scientific researchers in order to provide protection against the social threats in social networks are based on the investigation of social network threats. These measures are mainly oriented to the detection of malignant users and malicious application. Suggested solutions can be used for the maintenance of privacy of users by social network operators. In recent years, several research works have been carried out regarding the safety of social networks, that is, the protection of personal data, phishing, spams, detection of cloned or fake profiles and etc. areas. For example, Audience View interface was proposed for Facebook [37]. This interface allows the users to view their profiles as other users, for example, as “friends” or others. Such interface enables to detect which data is accessible for other users and to control the interface of personal data. The maintenance of user privacy in social networks is one of the important issues and FaceCloak architecture was proposed in this regard [38]. According to the architecture, the personal data of user is protected from social network users, as well as other users. For this purpose, FaceCloak stores the private information in a separate server in an encrypted format. Another important issue is the maintenance of the privacy of users for which a template for the creation of a tool for the maintenance of social privacy is proposed [39]. This template allows the automatic control of personal data of social network users. The majority of methods proposed regarding the fight against phishing attacks are based on the methods of detection of phishing websites and phishing links [40-42]. As the number of phishing attacks grows in social networks, several methods are developed for the identification of those. For instance, a system for detecting suspicious URL’s called WarningBird was developed for Twitter [43]. This system allows to detect phishing attacks “hidden” behind the redirecting URLs. A large number of solutions have been also proposed for spam detection in social networks. For example, an algorithm was developed for the detection of video-spams [44]. This algorithm allows for detecting spams in YouTube. Moreover, for the classification of spams in Twitter social network, the employment of the features of content and social network scheme was proposed [45]. Also, the algorithm of machine learning was employed for the detection of spams in social networks.[46]. The algorithm of machine learning allows to detect various types of spams. Different methods and approaches were proposed for the detection of cloned profiles in social networks. For instance, a specific tool was developed in order to determine whether the profiles of social network users have been subject to cloning attack [47]. An approach called CloneSpotter was proposed in order to detect the cloning attack on social network profiles [48]. This approach is based on the analysis of registration data of users available for social network operators. Various approaches have been proposed regarding the detection of fake profiles in social networks. Those approaches include various algorithms, methods and tools for the detection of fake profiles and the prevention of different Sybil attacks [49]. Although the goal of the algorithm of spotting the fake profiles and the algorithm protection from the Sybil attacks - the detection of fake profiles, is similar, some differences are present. The algorithm of spotting the fake profiles is oriented to the detection of fake profiles, and the cybercriminals possessing several fake profiles in social network. The algorithm of protection against Sybil attacks is aimed to identify the malignant persons creating several fake profiles in social networks. For this purpose, SybilGuard [50] and SybilLimit [51] protocols were developed. Alongside, an algorithm

78 www.jpis.az

Problems of information society, 2016, №2, 74-81 called SybilInfer was developed that allows to spot “real” and “fake” users in social networks [52]. Another approach is the employment of structural features of social networks for the verification of users; for this purpose, SybilRank tool was presented [53]. Conclusion Nowadays, social networks have become an integral part of everyday life of people. The majority of Internet users spend the large share of online activity in social networks. People establish contacts and share information (information, images and video) and experiences via social networks. Alongside, various threats exist in social networks. Hackers, swindlers and etc. use social networks as a tool for finding new “victims” and conducting their malignant acts. Hence, the analysis of existing threats in social networks and the ways of protection from those are of high topicality. The article analyzes the threats existing in social networks and the methods of protection. As a result of the analysis, it can be concluded that the threats existing in social networks pertain to two categories: traditional information security problems and threats with social aspects. It is also worth mentioning that, regardless the attribution of the problem to any group, those are directed towards the violation of private lives of users. It can be said that the violation of privacies of people in social networks, i.e in virtual reality, directly affects their real life. The outcomes of the analysis on the threats existing in social networks and the protection methods may facilitate the secure use of social networks by people and the selection of tools and solutions for the maintenance of the safety of users by social network operators. References 1. Stern J., Introduction to web 2.0 technologies, http://www.wlac.edu 2. Imamverdiyev Y. Social media and security issues / II Republican scientific-practical conference on multidisciplinary problems of Information security dedicated to 150 years’ anniversary of International Telecommunications Union, 2015, pp. 189-192. 3. http://www.statista.com/topics/1164/social-networks/ 4. Stringhini G., Kruegel C., Vigna G., Detecting spammers on social networks / Proc. of the 26th annual computer security applications conference, 2010, pp. 1-9. 5. Jacoby D., Facebook security phishing attack in the wild, https://securelist.com/blog/events/31951/facebook-security-phishing-attack-in-the-wild-14 6. https://en.wikipedia.org/wiki/Malware 7. https://en.wikipedia.org/wiki/Spamming/ 8. https://en.wikipedia.org/wiki/Phishing 9. Baltazar J., Costoya J., Flores R., The real face of koobface: The largest web 2.0 botnet explained, Trend Micro Res., 2009, vol. 5, no. 9, 10 p. 10. Amin T., Okhiria O., Lu J., An J., Facebook: A comprehensive analysis of phishing on a social system, EECE 412 Term Project Report, 2010, 6 p., http://www.courses.ece.ubc.ca/412/term_project/reports/ 2010/facebook.pdf 11. Cavit D. Microsoft security intelligence report, 2010, vol. 10, 89 p. http://www.microsoft.com/en-us/download/details.aspx?id=17030 12. Fire M., Katz G., and Elovici Y., Strangers intrusion detection-detecting spammers and fake proﬁles in social networks based on topology anomalies / ASE human journal, 2012, vol. 1, no. 1, pp. 26-39. 13. Lundeen R., Ou J., Rhodes T., New ways I’m going to hack your web app // Proc. of the Blackhat AD, 2011, pp. 1-11. 14. McMillan R., Researchers make wormy twitter attack / PCWorld, 2009, http://www.pcworld.idg.com.au/article/296382/researchers_make_wormy_twitter_attack/

www.jpis.az 79 Problems of information society, 2016, №2, 74-81

15. Krishnamurthy B., Wills C. E., On the leakage of personally identifiable information via online social networks // Proc. of the 2nd ACM workshop on online social networks, 2009, pp. 7-12. 16. Wondracek G., Holz T., Kirda E., and Kruegel C., A practical attack to de-anonymize social network users // Proc. of the security and privacy IEEE symposium, 2010, pp. 223-238. 17. Peled O., Fire M., Rokach L., Elovici Y. Entity matching in online social networks // Proc. of the international conference on social computing, 2013, pp. 339-344. 18. Facebook, Form 10-k (Annual Report)—Filed 02/01/13 for the Period Ending 12/31/12, 2013, 139 p., http://files.shareholder.com/downloads/AMDA-NJ5DZ/2301311196x0 xS1326801-13-3/1326801/1326801-13-3.pdf 19. The Faces of Facebook, http://www.app.thefacesoffacebook.com/ 20. Douceur J. R., The sybil attack // Proc. of the 1st international workshop on peer-to-peer systems, 2002, pp. 251-260, http://www.dl.acm.org/citation.cfm?id=646334.687813 21. Gao H. Detecting and characterizing social spam campaigns // Proc. of the 10th ACM SIGCOMM conference on Internet measurement, 2010, pp. 35-47. 22. Boshmaf Y., Muslukhov I., Beznosov K., and Ripeanu M., The socialbot network: When bots socialize for fame and money // Proc. of the 27th annual computer security applications conference, 2011, pp. 93-102. 23. Jeffries A., Facebook’s security check asks users to identify photos of friends’ dogs, Gummi Bears [UPDATED], 2010, http://readwrite.com/2010/08/04/facebooks_security_check_asks_users_to_identify_ph 24. Song A., Introducing login approvals, 2011, https://www.facebook.com/note.php?note_id=10150172618258920 25. Liu Y., Gummadi K., Krishnamurthy B., and Mislove A., Analyzing Facebook privacy settings: User expectations vs. reality // Proc. of the ACM SIGCOMM conference on Internet measurement conference, 2011, pp. 61-70. 26. Mahmood S., Desmedt Y., Poster: Preliminary analysis of google+’s privacy // Proc. of the 18th ACM conference on Computer and communications security, 2011, pp. 809-812. 27. Facebook, Facebook Help Center: Privacy, http://www.facebook.com/help/privacy 28. Axten S., Staying in control of your facebook logins, https://www.facebook.com/notes/facebook/staying-in-control-of-your-facebook- logins/389991097130 29. Fire M., Kagan D., Elyashar A., and Elovici Y., Friend or foe? Fake profile identification in online social networks / Springer journal of social network analysis and mining, 2014, vol.4 no.1, pp 194-216. 30. Chowdhury A., State of twitter spam, 2010, https://blog.twitter.com/2010/state-twitter-spam 31. Stein T., Chen E., and Mangla K., Facebook immune system // Proc. of the 4th workshop on social network systems, 2011, pp. 1–8. 32. Facebook, Report abuse or policy violations, https://www.facebook.com/report 33. Axon S., Facebook Will Add a Panic Button for U.K. Teens, Jul. 2010., http://www.mashable.com/2010/07/11/facebook-panic-button-ceop 34. AVG, Avg Privacyfix: http://www.privacyfix.com 35. Symantec, Norton Safe Web: https://www.facebook.com/appcenter/nortonsafeweb 36. McAfee, Mcafee Social Protection Beta: https://www.protectmediaonline.com 37. Lipford H. R., Besmer A., Watson J., Understanding privacy settings in facebook with an audience view // Proc. of the 1st conference on usability, psychology, and security, 2008, pp. 21-28. 38. Luo W., Xie Q, Hengartner U, FaceCloak: An architecture for user privacy on social networking sites, // Proc. of the international conference on computational science and engineering, 2009, vol. 3, pp. 26-33.

80 www.jpis.az

Problems of information society, 2016, №2, 74-81

39. Fang L., LeFevre K., Privacy wizards for social networking sites // Proc. of the 19th international conference on world wide web, 2010, pp. 351-360. 40. Garera S., Provos N., Chew M., Rubin A. D., A framework for detection and measurement of phishing attacks // Proc. of the ACM workshop on recurring malcode, 2007, pp. 1-8. 41. Ma J., L. Saul K., Savage S., Voelker G. M., Beyond blacklists: Learning to detect malicious web sites from suspicious urls // Proc. of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, 2009, pp. 1245-1254. 42. Xiang G., Hong J., Rose C. P., Cranor L., CANTINA+ A feature-rich machine learning framework for detecting phishing web sites / A ACM transactions on information and system security 2011, vol. 14, no. 2, pp. 1-28. 43. Lee S., Kim J., Warningbird: Detecting suspicious urls in twitter stream // Proc. Of the 19th Annual Network & Distributed System Security Symposium, 2012, pp. 1-13. 44. Benevenuto F., Rodrigues T., Almeida V., Almeida J., Gonzalves M., Detecting spammers and content promoters in online video social networks // Proc. of the 32nd international ACM SIGIR conference on research and development in information retrieval, 2009, pp. 620-627. 45. Wang A., Don’t follow me: Spam detection in twitter // Proc. of the international conference on security and cryptography, 2010, pp. 1-10. 46. Aggarwal A., Almeida J., Kumaraguru P., Detection of spam tipping behavior on foursquare // Proc. of the. 22nd international conference on World Wide Web, 2013, pp. 641-648. 47. Kontaxis G., Polakis I., Ioannidis S., Markatos E., Detecting social network proﬁle cloning // Proc. of the IEEE international conference on pervasive computing and communications workshops, 2011, pp. 295-300. 48. Shan Z., Cao H., Lv J., Yan C., and Liu A., Enhancing and identifying cloning attacks in online social networks // Proc. of the 7th international conference on ubiquitous information management and communication, 2013, pp. 17-19. 49. Koll D., Jun Li, Stein, J., Xiaoming Fu, On the state of OSN-based Sybil defenses // Proc. of the IFIP networking conference, 2014, pp. 1-9. 50. Yu H., Kaminsky M., Gibbons P., and Flaxman A., Sybilguard: Defending against sybil attacks via social networks // Proc. of the conference on applications, technologies, architectures, and protocols for computer communications, 2006, vol. 36, no. 4, pp. 267-278. 51. Yu H., Gibbons P. B., Kaminsky M., and Xiao F., Sybillimit: A nearoptimal social network defense against sybil attacks / IEEE/ACM transactions on networking, 2010, vol. 18, no. 3, pp. 885-898. 52. Danezis G. and Mittal P., Sybilinfer: Detecting sybil nodes using social networks // Proc. of the 16th annual network & distributed system security symposium, 2009, 16 p. 53. Cao Q., Sirivianos M., Yang X., Pregueiro T., Aiding the detection of fake accounts in large scale social online services // Proc. of the 9th USENIX conference on networked systems design and implementation, 2012, p. 15.

www.jpis.az 81 Problems of information society, 2016, №2, 82-90

Kamala K. Hashimova Institute of Information Technology of ANAS, Baku, Azerbaijan [email protected] PROSPECTS OF ADVERTISING-MARKETING OPTIMIZATION OF THE WEBSITES DURING THE SEARCH The paper conducts the optimization of advertising-marketing on the Internet while searching. The interaction between the customers and consumers during the optimization is investigated, and positive effect of optimization on the economy is presented. Key words: advertising-marketing, Internet, marketing optimization, SAS, SEO, RTB. Introduction Advertisements can be found in any area of the economy. In particular, the effectiveness of advertising should be considered on the Internet. As the number of Internet users is increased, optimization of advertising - marketing attaches the importance. Executives for advertising- marketing chair the organization as a whole, and ensure its effectiveness. An effective advertising-marketing leads to an increase in sales of the advertised product that has a positive impact on the economic development of the country. Currently, the economy is hesitant area to be approached by everyone. The increase in the number of online users, assimilating marketing, involving new generation to the economy is satisfactory. Today, optimizing marketing strategy is of great importance. During the optimization, the marketing should be based on several stages. These stages are very effective in an economic terms [1] .  Optimizing key marketing aspects of the acceleration-PPC (Pay Per Click);  Lead generation experience;  User Experience. The importance of PPC optimizing The main purpose of algorithms complexity is competitiveness. The need for PPC optimization has a few reasons: . Usage of high-quality algorithm accounts; . How consumers search for products and services; . Lack of delay. Optimization of PPC pages is important for attracting new customers. This method gives a rise to progress quickly, non-optimized pages lose customer. Table 1. Comparison of an optimized page to non-optimized one Before page optimizing After page optimizing  Having too much access;  Frequent apply to users by reducing the  Not-responding page for a long time; number of access points;  Asking users for filling the complex  Collect purposeful pages to memory; forms;  Reduce to a minimum the amount of  Lack of unformatted key words among the form; the technical information;  Use of key words on Heading and list;  Impact on users with an uncertain call.  Involve users with an attractive title;  Set a confidential relationship with the other party by providing warranty, certificate printing and indication of the reliability.

82 www.jpis.az

Problems of information society, 2016, №2, 82-90

During the advertising-marketing on the Internet, to develop site's unique strategy optimization is important for users. Small changes can give a different result in search engine systems and optimization. Search optimization – it is the top position of the site for effective surveys in search engines. Visitors of the site in search engines are high quality and targeted audiences. Users, requesting for search queries in search database, state their intentions in a commodity, service or in information. During the search through the site, they immediately get the desired product or service they are able to order. Search databases show the outputs of search according to the specified rules: sites suiting for search algorithm, covering user survey maximum is on the first place. In addition, algorithm estimates the site, taking into account all the features, such as composition, structure, and technical aspects, if all the requirements of search algorithms met with search results and site to be adapted in accordance with them; position of the site will increase significantly. During the optimization of advertising-marketing on the Internet, the search is carried out on the request of "the core of requests" (performance list, search queries when the potential customers apply search engine)[2]. The features of search optimization in the Internet advertising-marketing:  Scope of the audience – interested in a search engine query, which includes user's product, service or information. Handy search engine sites can be used to buy the products or to order services.  Cheap price attracts customer - compared to traditional advertising channels, customer involvement embraces television, radio and the press.  While Internet advertising-marketing search optimization requires small number of expenses, and brings relatively numerous and qualitative audience to the site. Commencing from the design, the Internet optimization requires a huge content and a rich web structure. Designer uses relevant methods to provide the users' needs, respond to the information in required form and carefully learn easy navigation. The data is collected in a website in accordance with the legislation. Management board is responsible for the confidentiality of the data. Database compiles collected or uncollected data on identifying personality and used, if necessary. Certain identity implies the name, mailing address, telephone number, e-mail and the IP address. If the number of visitors accessed a website and their presence time is unknown, this case is called data on the personal identification is non-available. As other databases, queries between users and customers, and reviews can be shown. Web-browser is a device used to stream the data to the web-server and accessing to website. It embraces volume, output of the used and transmitted data, referring domains and data of IP addresses stored on the server file. Those who are willing to innovate to the trade over the Internet, to develop new technologies, and new projects, to share, and to train producers should be aware of the followings [3].  SEO (Search Engine Optimization) - web sites, search engine optimization;  RTB (Real Time Bidding) - new technology which constitutes the real-time ad auction announcements of online advertising;  SEM (Search Engine Marketing) - advertising, marketing services on search engines;  SMM (Social Media Marketing) - Social media that is used by many people, i.e. marketing, advertising and promotional services over networks;  Online advertising – boosting promotional videos, mass e-mail and SMS fulfillments, and activities such as online request and promotional marketing. SEO search optimization SEO (search engine optimization) or website optimization provides forefront while searching. When a customer is looking for information about a service or product on search sites,

www.jpis.az 83 Problems of information society, 2016, №2, 82-90 he uses key words, then, a few site names appear on the screen. Forefront sites must be applied. This method of online advertising is one of the most effective ways to attract customers. There are a few types of appearance of the page name in the first page of search results. Some of the steps that must be taken into account in order to achieve outcome, are divided into two parts: 1) Internal optimization; 2) External optimization. Internal optimization includes the work with customer, site structure, and links. External optimization is increasing the link number. This process has been ranked by means of all search engines. Ranking is definition of the line and response for user in search system according to rank, authority, or eligibility. By learning ranking mechanism, many questions can be responded. For example, two similar- search sites on the same topic; one in the front at the top, and the other is located on the third page. Usually, all the sites are indexed by search engines. Each search system conducts a search according its principles. The difference is that, in fact, the sites lie in different places in different search systems. In case a site is trusted by the search systems, changing the algorithm will not change its position. It plays a key role in the internal and external ranking. Ranking is based on site relevance. Each search system has its own principles for survey adoption. There are two important criteria in establishing compliance with the survey of the site. The first criterion is ranked by its content. The eligibility is derived from the lexical- semantic analysis of the text placed on the site [5]. The second criterion is a mere formality. This conformity determination of eligibility is based on algorithms of search systems that have been included in the program. The result of evaluation is not stable, and has a variable growth. Occasionally, the employees change the system algorithms, in order not to access the wrong sites through the barrier filters. If the site is trusted by the search systems, then the change of algorithm will not change its position in the ranking. Distributed or centralized information processing exists at sales outlets in order to provide customer services. Operational reporting and authorization data storage are managed by means of the analytical sub-system. RTB Real Time Bidding – new technology is eligible for organizing auctions of advertising in the field of online advertising in real time. RTB had a major influence on the digital advertising market in the world. RTB is widely used in the USA being the leaders at advertising technology platform. Though European Union accepts RTB more slowly, but the demand for mobile connectivity is increasing here. RTB helps advertising deploy on the Internet. Advertising network providing RTB service after receiving a request for ad display: 1. Survey is classified according to the maximum number of parameters; data about the user (if enabled), the area which is included, time, and the target is accepted prior to purchase. 2. Additional data is collected from the base of selected ads mechanism according to the particular criteria. 3. Survey response on user is transmitted to the advertiser, and the share is obtained. By means of this share they get opportunity to next display. 4. The highest share is determined among the obtained responses, the next stage is established. 5. Announcement is bought from the winner advertiser and given to the user as a response to the request.

84 www.jpis.az

Problems of information society, 2016, №2, 82-90

percent

RTB Ad server sistemi(Real Time Bidding) Request for ad Ad

Data on Ad server

request

Figure 1. Advertising and marketing of search optimization Caution on the following areas of advertising-marketing search optimization: . Motion; . Contact form; . Speed; . Title. Increasing the number of applications, optimization location on the first page makes positive impact on the audience. Recurrence of the pages should be persistent [6]. What is marketing optimization? Marketing optimization is the process of improvement of the organization's marketing performance in order to achieve maximum outputs. Marketing optimization is widely used in the development of marketing. The theory of the Web-optimization hierarchy: In the absence of Websites (WPO - web presence optimization), idea of creating a hierarchy of optimization came from the belief. While search, ground-based principle is required before achieving success. Usual search is at the four intersections - optimized marketing, social media, all changes of Google algorithm and marketing. Total tactics in marketing is comprehensive marketing optimization. If the optimized marketing to be applied, during campaign results will be as follows: . Improved and extended ranking while usual search; . Positive impressions; . Click-increased probability; . Changes; Optimized marketing plan covers Google algorithm with four major changes since previous year: a) About updates - press releases on Google innovations, blogs, case studies, latest news and events; b) Google upgrade – is the filter of the given search. Optimized content distributed for true, reliable sources, to assist for establishing of strong anti-citations; c) Google + - is a social and social-media network about the links. These links prove the demand is provided. Google's social network, Google+ and its algorithm actually takes into account the search;

www.jpis.az 85 Problems of information society, 2016, №2, 82-90

d) Supremacy of Your World request - than any other social network, for example, including Facebook or Twitter, Google + is more reliable. A long-term service can be obtained here by openly sharing ideas [7].

Figure 2. Intersection of search optimization How does SAS assist for Marketing optimization? SAS (Statistical Analysis System) provides business ties by delivering optimization and identifying customers, and keeps in touch with each of them separately. For example, taking into account the probability of the responses to customers, outputs expenses, political reflection, resource, budget constraints, etc. Why SAS Marketing Optimization is important? Transmitting the optimization, SAS increases the ratings of marketing campaigns by allowing each customer to identify the best proposals. Moreover, it eliminates business constraints such as budgets distribution increasing the capacity of the channel contacts, changing the outputs in link policy by allowing discussion [8]. SAS marketing can be used by marketing campaign managers, business analysts, managers for segment and analysts. The effect of the optimization is designed to give a guarantee the huge economic results of marketing. Relying on marketing, the organization representative directly messages the vulnerable customer by creating a noisy marketing in order to present the least as much. In this case, mutual effect that forecasts segmentation, modeling and testing can improve the efficiency. The marketing result within a day is not clear. Marketers should study trade issues competing through sections. Being aware of the numerous marketing programs for limitations, budget oversight and customer relations should be specially carried out. Diversified ventures lay across the choice before millions of customers. In order to maximize income or (Return On Investment - ROI) within a budget, it is necessary to carry out policy with experience and intuition. As a result of maximum extent of the relationship with each customer individually, and careful approach in presenting SAS Optimization, the profit of the organization is maximized while applying mathematical methods. Key words option Key words option is the key phase in SEO. If there is no right choice it is difficult for the consumer to get high rankings in search engines. Wrong choice of key words or phrases may yield unexpected results. Next steps should be considered during key words option:

86 www.jpis.az

Problems of information society, 2016, №2, 82-90

1. The brain product - must take into account that potential buyers will write the name of the product (pronounced options, different word forms, synonyms) due to searching for the prices of goods and find for the proposed product. 2. New and potential buyers’ request – is the method of posting as more keywords as possible into the list. By means of website for raise of traffic is essential to increase sale amount. 3. Action directions option – is to draw up the table or graph. Action direction reflects traffic comparison, its relevance and changes to them, helps to gather additional information to make the right decision. 4. Execution, verification and analysis - selected keywords and programs are designed to track included web-changes. 5. Special program – analytic programs are available to measure traffic amount and track of changes in sites. For example, Wordtracker (www.wordtracker.com) and Overture (www.overture.com) offer information on the number of requests made by users. Using the software database, the appropriate keywords for any business can be determined. Table 2. Keywords selected by means of software Keyword Volume KEI Comp. IAAT IT Country inexistent 26601171 96.57 7.33 89 197753 GLOBAL

movies 20226091 80.57 51.6 712635 199564908 GLOBAL youtube 10585128 79.06 49.76 602806 44606390 GLOBAL facebook 7844123 78.03 49.94 612976 52456127 GLOBAL google 7579933 75.89 56.81 1109804 189408257 GLOBAL followerscounter 6628518 97.92 3.21 2 18 GLOBAL comcom 5942025 93.87 9.88 352 105824 GLOBAL craigslist 5811554 85.2 27.52 39392 2985349 GLOBAL search 5597574 74.72 57.15 1140637 1056711615 GLOBAL kinox 4833876 91.37 13.85 1667 263467 GLOBAL

Both programs are completely suitable for comparison of amount and results. Overture unites short and meaningful ideas. Although the volume of Wordtracker is small, it provides detailed information [9]. For example: Overture portal

colored book pictured colored book electron book book

Wordtracker portal loans of the housing loans housing loans house houses creditors

Figure 3. Result indicators of search web-sites

www.jpis.az 87 Problems of information society, 2016, №2, 82-90

Requests appear briefly in large letters, in plural and word form. Data accuracy rate is more useful for the analysis of surveys. Usage of large letters in search engines in advertising- marketing on the Internet does not give a different result. Plural forms of words collects brief opinions of different surveys. When we compare the number of users who have the same requests at the same time, the result can be extremely inaccurate. The preference is to use different words. Directing consists of usual measure of the traffic amount and optimal key words. Efficiency rate is measured by the percentage of users who are shopping by applying:  estimating the number of users who applied within a month according to proposed traffic-survey;  estimating the customers - average assessment of buyer’s comparison in search of cheaper-expensive;  Key words competitiveness – calculation degree of the competition for chosen word. It is the financial parameter in SEO sector due to number of competitors including the impact of the references. If the algorithm was on the focus during investigation of the optimization in search engines in advertising-marketing on the Internet, in recent years, it is enough to make use of search marketing aids. The creation of a normal relationship with readers is very important in optimization. User should take into account the limited opportunities when choosing keywords. For example, survey for “small tablets” will be a lower level of competition. Optimization of the largest surveys often faces with big stuck. Typically, permanent buyers applying to the site with accurate and concrete key words get limited number of results. Less sorting of information is readily available. There are countless optimizers started creating content optimization. They think of how many keywords they use and where to post them. Sometimes, being addicted to the keywords, the creators of the web-resource forget about the quality of the content. In this case, they do not achieve the sought result. Sometimes during the Internet advertising-marketing optimization, we come across meaning lines that are not similar. If the user quits without being acquainted or without attempting to its usage, it is considered negative. The coordination should be appropriate when the keyword relevant to the content is typed. Search engines are complicated and important for creating websites. Establishing stable flow of traffic in order to get the effectiveness of websites optimization is enough on the Internet advertising-marketing. It is known that users are basically using search engines. A site referred by a user during advertising-marketing searching optimization should inform the user. Home page of the site should awake imagination on the user on the site, and guide him/her how to find out useful data [10]. Sections cover a variety of site topics; sub-sections provide more detailed information on the topics. Everything may seem simple, but experience shows against. For this reason, the scheme of the site is developed in advance. Keywords can define the weight of the site. Sites are associated with one another during the optimization. The location of the pages in hosting is not significant. Site design and internal posting of sections may be different depending on site creation, but the overall structure of the site must comply with this model.

88 www.jpis.az

Problems of information society, 2016, №2, 82-90

Home page

Section 1 Section 2 Section 3

Bölmə

Subsection Subsection Subsection Subsection

Subsection Subsection Subsection

Figure 3. The simple structure of the site and its units Three-level model structure of a simple site and its sections: Section 1 – Home page Section 2 – Sections on topics Section 3 – small subsections The level of the site is identified by the number of citations. First level – home page; Second level – citations to home page; Third level - citations from the second level pages. Thus, two issues should be considered during advertising -marketing optimization on the Internet: a) To involve users to use search engine by writing correct title relevant to request; b) To turn a regular readers into a buyer. The full download of the page is more important for users. Most part of users leaves the site when it has not downloaded within three seconds [11]. During the estimation of the web-sites algorithms, search engines and their indexes are utilized. As the SEO is constantly developing those who are engaged in websites should cope with these changes. A lot of search assessment methods are urgent today - but after a couple of years can be completely out of date. Moreover, these methods that do not produce positive results two years ago, even now can meet the demand. Conclusion Search optimization of advertising-marketing on the Internet highlights the visibility of company in the network with overall approach, and without requiring large expenditures. Organization should think not only as a supplier of goods and services, but also think about its image. One of the country's economies is advertising- marketing on the Internet. Accordingly, advertising-marketing organization and its effectiveness is necessary. Currently, the Internet advertising embraces vital ads functions and most attendees of market utilizes it. Each user of the Internet should provide safety. When the information is used, a page shall open, taking into account the compatibility of the brief announcements with the requested data, during the optimization.

www.jpis.az 89 Problems of information society, 2016, №2, 82-90

Literature 1. Golik V.S. Efficiency of internet-marketing in business, Dikta, 2008, 196. p. 2. Baykov V. D. Internet: searching for information on promotion of sites, St. Petersburgh, 2000, 288 p. 3. Lunkim T. Traditional System of Kuki Administration, in T. Haokip (ed.). The Kukis of Northeast India: Politics and Culture, New Delhi: Bookwell, 2013, Chapter 1, 46 p. 4. Sevostyanov I. О. Searching optimiziation. Practical management for promotion of sites on Internet, St. Petersburgh, 2010, p. 182-187. 5. Belch G., Advertising and Promotion: An Integrated Marketing Communications, 4th ed, NY, McGraw-Hill Book Co, 2003, 62 p. 6. Cherniy А.I., World information market // НТИ in abroad, М., 1998, p. 230-234. 7. http://www.proview.ru/simple_and_quick_solution_of_main_tasks_in_search 8. http://www.proview.ru/how_to_conduct_keyword_research 9. http://www.bruceclay.com/jp 10. http://www.seoded.ru/beginner/raskrutka.html 11. http://www.ashmanov.com/marketing/seo/what

90 www.jpis.az

Problems of information society, 2016, №2, 91-100

Kamila A.Valiyeva Institute of Linguistics after Nasimi of ANAS, Baku, Azerbaijan [email protected] MODERN AREAS OF COMPUTATIONAL LINGUISTICS The paper analyzes the main trends of computer linguistics - natural language processing, corpus linguistics, computer-aided dictionary compiling, computer-aided learning and recognition of languages, including machine translation issues. Key words: computational linguistics, natural language processing, machine translation, corpus linguistics. Introduction Computer Linguistics, which has been emerged at the intersection of computer science and linguistics, is one of the fastest growing areas of science in the modern era. As a branch of applied linguistics, it studies linguistic basics of computer science, and computer-aided modeling of thinking and language. Thus, it aims at addressing the issues related to the establishment and improvement of common language in the information presentation, including modeling of information systems and natural language interface, which is one of the main issues of artificial intelligence. Developed in the 60s of the twentieth century, computer linguistics first focused on theoretical linguistic models. However, later the subject of the study has aimed at a broader direction - machine learning with the application of statistical methods, including texts processing, and a number of heuristic issues [1]. As we mentioned above, as computer linguistics develops mathematical model of natural languages, and, of course, the key points here are the use and development of language for multi- purpose computer systems. In this regard, computer linguistics tackles the following issues: 1. natural language processing - syntactic, morphologic, and semantic text analysis; 2. corpus linguistics (generation and use of e-corps of the texts); 3. electronic dictionary compiling (thesaurus, automated translation dictionaries, encyclopedias, orthographic, explanatory, terminological and field-specific dictionaries, and spelling dictionaries for automated error detection and etc.); 4. automated text translation systems (Dilmanc, Google Translate etc.); 5. fact extraction and text mining; 6. auto-summarizing - included into Microsoft Word; 7. knowledge (expert) systems; 8. dialogue systems; 9. optical character recognition (OCR, FineReader); 10. automated speech recognition; 11. automated speech synthesis; 12. development of information retrieval systems [2]. Natural language processing is a branch of artificial intelligence and mathematical linguistics, and studies computer-aided analysis and synthesis of any natural language. In short, interface language (human computer interaction) has been established for computer in the process of theoretical language processing from the point of view, which is not an easy task. Natural language perception (understanding) requires the rich knowledge about the world, hence one of the main issues in artificial intelligence is to introduce this “perception” to a computer. For example, the comprehension of the Azerbaijani text depends on word order, homonyms, synonyms, punctuation and emphasis. At this point, formal models of the listed criteria for text comprehension have to be developed.

www.jpis.az 91 Problems of information society , 2016, №2, 91-100

Corpus Linguistics (CD) is an applied branch of linguistics that explores the text corpus and use. The term was introduced in 1960 with the emergence of corpuses in practice. “Linguistic corpus” implies a set of texts with certain principles and standards. Corpus generation aims at solving various linguistic issues (such as, graphic, grammatical and lexical text analysis). The first major in the world linguistics - Braunov corpus, was established in 1960. According to the corpus model, the Russian frequency dictionary, containing more than one million words, was compiled by Zasorina [3]. The rapid development of computer technology has contributed to the development of large-scale national corps, as British National Corpus developed at the University of Birmingham, and Ershov’s Machine fund at the former USSR. Currently, the Russian national corps contains 300 million word combinations [2]. The creation of a national corpus of the Azerbaijani language is still remains one of the issues to be resolved. The problem solution: computer-aided dictionary compiling The rapid development of computers has contributed to the automation of mental activity and opened broad prospects. Research carried out through computer released the linguists from the mechanical, tedious and labor-intensive works, such as material grouping, typifying, inventorying, and editing. In the 50s of the last century, two major laboratories equipped with computers were established in Europe. One of them, located in Bezanson, is the lexicological analysis laboratory at the French dictionary learning center. Another is the laboratory of linguistics center on philological analysis, located in Italy. Moreover, in the Netherlands, a scientist F.de Tollener was conducted computer-aided lexicographical research [3]. Most of the Russian scientific centers have conducted many researches regarding computer application. These centers have developed different types of computer-aided dictionaries, such as the dictionaries of frequency of words used in the text, including concordances containing a list of specific expressions in the text, and the reverse-dictionaries listed in alphabetical order depending on the word endings, rhyme dictionaries, and automated dictionaries designed for machine translation system, and so on. The book “Electronic computing machines in linguistics” by Pines V.Y. and Mahmudov M. provides detailed information about the above-mentioned dictionaries [4]. In a case study, we will review only the computer-aided dictionary compiling in the Azerbaijani linguistics. Before touching upon this matter, it is worth providing brief information about a computer and its performance. Obviously, “human-machine” system resolves the issues in the following steps [5]. 1. problem statement; 2. mathematical formulation of the problem; 3. preparation of the material to be included in the computer (initial data), i.e., coding); 4. selecting appropriate methods to solve the problem; 5. developing an algorithm and its block-scheme; 6. programming the algorithm; 7. computer-aided problem solving; 8. analyzing the results. These steps can be briefly characterized as follows: “Mathematical statement of the problem” – data enumeration (in this case, materials depending on the nature of our issue), i.e. coding, and solution of its components. Sequenced solution of the components completes the issue. Indeed, the most optimal solution method should be selected. For example, a typification method is typical for compiling dictionaries of frequency. An algorithm should be developed based on the selected method.

92 www.jpis.az

Problems of information society, 2016, №2, 91-100

Many important frequency dictionaries of the Azerbaijani linguistics have been compiled with the help of mathematical and statistical methods. Obviously, frequency dictionaries record the frequency of words and their use in the texts. A number of difficulties occur while compiling these dictionaries. First, the text should be selected in such a manner that literary language norms are adequately incorporated. It is not an easy process. Second, more complicated task is a selection of the overall volume of texts. The main source for the frequency dictionary of the Azerbaijani language is a newspaper. Hundred thousand words have been selected from random texts. The use of frequency dictionaries in automated data processing - automated translation, summarizing, polygraph, forensic science, and in identifying the anonymous authors of the articles is of great importance. As it is noted earlier, the first frequency dictionary was published in 1997 [6]. It was revised and published in 2004 [7]. In 2010, the frequency dictionary of the Azerbaijani language was published [8]. All the methods of the Azerbaijani language have been represented here. Lingua-statistic texts analysis involved about 50 million words. Generally, the dictionary contained nearly 12,000 word roots. The frequency dictionary of the Azerbaijani language plays a role of an invaluable resource in solving many problems of general lexicology and language teaching as a statistical model. Furthermore, the application of statistical methods in the study of historical monuments is of undeniable importance. The Polish scientist Chekanovsky Y. used this method for the detection of the kinship among the groups of Indo-European languages for the first time in 1927 in the world linguistics [9]. In 1948, the American researcher Svodesh M. studied dictionary fund of all languages using lexical and statistical method and concluded that, dictionary fund of all languages is changing slowly, at a steady pace [9]. In addition, statistical methods play an important role in the study of language typology. Alphabetical-frequency and reverse-alphabetical list of the critical text of 1988 year issue of “The Book of Dede Korkut” (authors: Zeynalov F. and Alizadeh S.) was published with regard to the computer-aided study of historical monuments, including written monuments of the Azerbaijan linguistics [10]. The frequency list here covers ten thousand word forms. The words of the legend are listed in a condensed format according to its frequency of use. The list of alphabetical-frequency the word forms are not arranged according to the frequency, but to the alphabet. The compiled list is of great importance for professionals involved in the language history. Statistical and distributive analysis of the classical heritage of Fuzuli’s poetry has also been explored. Of course, it is of great importance for revealing the stylistic features of classic literature authors, and for the identification of unknown authors of the works by comparing the frequency. Moreover, “Reverse- dictionary of the Azerbaijani language” widely used in solving many problems of the Azerbaijan linguistics has been drawn up [11]. What is reverse-dictionary and how it differs from usual dictionaries (bilingual, explanatory, etc.)? Reverse-dictionary differs from usual dictionaries in structure. In ordinary dictionaries, the words are arranged in alphabetical order according to the initial letters. While in reverse- dictionaries, they are arranged in the alphabetical order according to the last letters. “Reverse-dictionary of the Azerbaijani language” has been compiled based on the “Spelling dictionary of the Azerbaijani language”. Moreover, the words that are not included to the “Spelling dictionary of the Azerbaijani language” have been taken from the “Azerbaijani- Russian” dictionary and included to the reverse-dictionary. “Reverse-dictionary of the Azerbaijani language” is one of the smartest among the Turkic language dictionaries in terms of volume.

www.jpis.az 93 Problems of information society , 2016, №2, 91-100

Electronic dictionaries - different types of dictionaries in computer memory (compiled depending on the purpose). By the way, it should be noted that, depending on the purpose, electronic dictionaries differ from the computer dictionaries. Computer dictionaries are developed for the users in order to take advantage of computer applications. Various dictionaries are available on the Internet, such as ABBYY Lingvo, Poliglot (Azeri Dictionaries), Lingvo, thesaurus, ontology, thesaurus, spelling, reverse-dictionaries and so on. Machine learning - a branch of artificial intelligence aimed at teaching and developing algorithm models. As an area of artificial intelligence, it was founded at the Dartmouth conference in 1958 with the report made by Solomonov R. on training without teacher, i.e. computer-aided training. Machine learning of mathematical analysis of algorithms has contributed to the emergence of computer training as part of theoretical computer science. This area includes image recognition issues: recognition of symbols, manuscripts and speech, text mining, computer-aided vision related to robotics. Computer-aided language learning Currently, computer-aided study of the various systematic languages is very popular in the language teaching. Hundreds electronic dictionaries, machine translation for various languages, teaching software systems, expert systems, and information retrieval systems have been put into operation so far. Here, we will briefly inform about the computer-aided learning of agglutinative languages. We will mainly focus on linguistic analysis of the text, which is one of the most important steps in computer-aided languages learning. The Azerbaijani language is given here as an example of agglutinative language. By the way, let’s clarify some details of the processes taking place during computer-aided learning of natural languages. What language learning tools are usually needed? Of course, first of all, vocabulary, grammar and conversation manual are very important. When it comes to computer-aided language learning, the situation changes. It is important to introduce an electronic version of the dictionary, to reduce the grammar to a formal structure, and information-retrieval systems, dialogue systems, expert systems, dictionary sounding, including query system should be developed. In other words, linguistic provision and information technology issues have to be addressed, and we mainly focus on this matters. It should be noted that bilingual dictionaries designed for the learning systems are quite different from the usual ones. Usual vocabularies include words and their meanings. Whereas the dictionaries designed specifically for the mentioned system are not limited with the words and their meanings. They take into account the character of the given problems. For example, along with the words and their meanings for machine translation and language teaching, they include what part of speech it belongs to, sound decline (grapheme decline), ending with the letters g, k, t, homonymic syntactic and semantic features, the presence in a fixed word combinations, and verbs grouping (valency and being effective or ineffective). It should also be noted that since the electronic dictionaries are open systems, new words can be added her anytime. The dictionaries designed for learning systems provide the image descriptions of the words and their sounding using visual tools. When it comes to the suffixes included into the database, they are grouped based on the parts of speech they belong to. Moreover, field-specific terminological dictionaries are included into the database as linguistic software. Unlike the training courses, learning programs based on phonology provide the system of vowels and consonants, and the harmony rules expressed with sound examples in the form of a dialogue. Learning program system based on lexicology mainly contains word creativity. The main emphasis is put on the formal description of the word synthesis in the Azerbaijani language [12].

94 www.jpis.az

Problems of information society, 2016, №2, 91-100

For a formal description of the word synthesis, the harmony of the suffixes with the word forms, interaction of roots and suffixes, position of the suffixes relative to the roots, the harmony of the suffixes in the word positions, and the algorithm issues should be designed as the query-dialog. Learning program system based on morphology includes the parts of speech (nouns, adjectives, pronouns, numerals, adverbs, and verbs). Of course, the data about the parts of speech are included into the computer memory in formalized form. At the same time, auxiliary parts of speech: goshma and auxiliary names, connectors, particles, modal words, binding, exclamation, imitation words, imperative words, children’s words, and rhythmic words are included into the linguistic database [13]. Learning program system based on phraseology includes the key factors proving the existence of the language units of the fixed phraseology and the classification of phraseology units [14]. Lexical analysis implies the separation of the texts into the paragraphs, sentences and words, the definition of the language of the text, the determination of the sentence types (with the help of key words and the alphabet), and the definition of the types of lexical expressions (jargons, spells, etc.). The realization of these issues on computer is not so easy. Morphological analysis deals with splitting the words of the text into the differential fragments, and defining which part of speech they belong to, in short, automated definition of features. Syntactical analysis defines the relationship between the words and sentences in the text and the position between them. Semantic analysis is the most complicated process, which determines the meaning and the content of the sentences in the text. This logic-based analysis defines the logical dependence among the words, and the problem given in this regard is formalized. Since the semantics is impossible to be formed through traditional methods, special quality expert systems should be developed. These systems define how properly the meaning is found and check its quality. It also depends on the creation of artificial intelligence systems. The development of science in this regard is still pending. Effective use of search engines through available information technologies is not up to date yet. Syntactic analysis of language teaching systems understood as the search for the main and second members of the Azerbaijani texts and their syntactic relationships. Of course, they should be reduced to a formal form for such a search in the dialogue system. Automated text translation As detailed information about the automated or machine translation (MT) is given in the Azerbaijan linguistics, we will focus on new trend of machine translation in the Azerbaijan linguistics - statistical machine translation, which has to be resolved [2]. Machine translation algorithms are established in accordance with two different principles in the literature – basing on special rules and statistics. Statistical machine translation bases on the comparison of a large-scaled language pair. The language pair is an expression of the sentences of a text in one language with the respective sentences in another language. As if the text options written in two languages and their translation. The more language pairs are, the more accurate the compliance coefficient is, and consecutively the higher the quality of statistical interpretation. In some cases, the hybrid machine translation is used. Hybrid machine translation uses various respective options of MT. When it comes to the computer linguistics in Azerbaijan, mathematical methods have been used since the 60s of the last century - with the publication of the book “Mathematical linguistics in education” by Garayeva M. [15]. Research studies have been implemented on all branches (machine translation, the development of formal models of the Azerbaijani language, and language study with statistical

www.jpis.az 95 Problems of information society , 2016, №2, 91-100 methods) of mathematical linguistics founded by Garayeva M. In 1976, a new area of the Azerbaijani linguistics - a group of applied linguistics was found at the Institute of Linguistics after Nasimi. Under Pines’s leadership, this group began to deal with the problems of applied linguistics, including machine translation (formal description of word synthesis, morphological analysis, automated syntactic analysis and synthesis of texts, automated text editing etc.), formal modeling (modeling the structure of verb forms in Turkish, automated synthesis model, etc.), and statistical analysis of ancient monuments with statistical methods. In 1979, Akhundov A. wrote his work “Mathematical linguistics” [16]. His recently published work “About some features of the applying the structural and mathematical linguistics methods in the Turkic languages” [17] is also of great interest. Azerbaijani scientists are successfully using above-mentioned methods in their research. Thus, Akhundov A., Valiyev A. and Melnikov G. worked on the axiomatic method [16-20]; Valiyeva K., Pines V., Mahmudov M., Amirov Z., Fatullayev A., Khalili A., Guliyeva Z. and Iskandarova N. – on modeling method [12, 21-27]; Melnikov G., Valiyeva K., Valiyev A. and Mahmudov M. – on theoretical linguistic methods[28-32]; Valiyeva K., and Mahmudov M. – on the use of the theory of sets, automats and algorithms [6, 7, 33, 34], Pines V., Valiyeva K., Mahmudov M., Rahmanov J., Sultanov V., Mammadova S. and Shikhiyeva I. – on the statistical method [35-40]. It should be noted that we will no limited to the work done and will be involved in future research, such as oral speech recognition, computer-aided language training, development of video and electronic dictionaries and etc. Computer-aided natural language recognition As mentioned above, the importance of the human-machine relationships in regards to in- depth study of the structure of the natural languages, developing the most complete and important theory for the communication that reflects all aspects of the communicative act and the inevitability of involving the linguists to these issues to satisfy the users’ needs appeared at late 60s. Evidently, the main communication in man-machine relationship must be in the form of dialogue. Depending on the purpose, the dialogue can be at three levels: global, thematic and local levels. Global level communication is determined by the common characteristics of the given question; the structure of the thematic dialogue depends on the specific algorithmic solution of the problem, and finally, local level dialog involves the various stages of the mutual dialogue between the participants [41]. As for the computer-aided recognition of natural languages, speech recognition and text recognition should be noted here. Since both areas are studied separately and cover a wide scope, we will only provide a brief introduction to these recognitions. For automated text recognition, naturally, first of all, linguistic support should be provided. Linguistic support includes the following issues. 1. Automated (machine-aided) phonological recognition of graphemes; 2. Automated (machine-aided) recognition of morphemes; 3. syntactic recognition of the chain of morphemes; Grapheme level is the conversion of the respective alphabet of the language (graphemes of the text) into machine language (machine language alphabet consists of two figures: zero-0 and one-1), that is coding. The alphabet of the Azerbaijani language consists of 32 graphemes. So, 32 graphemes are expressed in the machine figures consisting of 0 and 1, i.e. encoded. As for the automated recognition of morphemes, as the morphemes are composed of the graphemes’ chain, their coding is expressed with the sum of the respective graphemes. It is known that the root and suffix morphemes are distinguished. It should be noted that root and suffix morphemes must be included into the memory of the computer.

96 www.jpis.az

Problems of information society, 2016, №2, 91-100

For syntactic recognition of the morphemes the phraseological expressions, encoded sintagmas of the fixed word phrases should also be included in the computer memory. Along with the linguistic support, mathematical support should also be provided for computer-aided recognition of the natural languages. Of course, for mathematical support recognition applications should be based on the solution algorithm of the given problem. Recognition programs of different system languages are currently available. The written texts are included in the computer through the recently developed scanner facilities, and then, the texts are read with the use of recognition programs, and the proper operations are enabled in regard with the given problem. Computer-aided recognition of the spoken language, as well as the oral speech differ from the recognition of the written texts. However, the similarities of solution method are manifested in the language approaching level. Here, instead of the graphemes, the phonemes should be sounded in order to include them into the linguistic database. On the one hand, the should be studied from the phraseology and acoustic aspects for their introduction, and on the other hand, all the data related to the phoneme system, including the key signs and status of phonemes, their perceptive and functional aspects and correlation should be included into the linguistic database. In addition, the allophonic variations and complexion of the phonemes, their types, orthoepy and recognition function should be considered when generating the linguistic support. The syllables play a key role in the speech recognition. Therefore, the structure of syllables should be thoroughly studied and their formal models should be developed. One of the key factors in the speech recognition is the emphasis. According to the linguistics literature, it is clear that in different languages the emphasis depends on its function. For example, in the Russian language, each word has its own emphasis. Because, in this language, the emphasis has a ‘culminative’ function. However, as the emphasis carries the delimitative function in the Turkic languages, it is put on the final syllable of the word [42]. Speech recognition software defines the emphasis with the higher sounding of the latter syllable. In this context, as syllable models, intonation, and the rhyme of the words in the speech recognition is another research subject, we will not touch upon these problems. It should be noted that the reading of ancient manuscripts is one of the interesting issues of artificial intelligence. The survey on the reading hieroglyphic writings of Maya can be cited as an example. Living in the American continent, and having its own writing system, these people have been deprived of their culture. They lost their books, and rich library was burned by the Spanish invaders [43]. Today this manuscript is being preserved in Dresden, Madrid and Paris. Scientists from several countries, especially the United States, France and Germany, have worked on deciphering this manuscript, but haven’t achieved any result. In 1951, the Soviet scientist Knorozov Y. opened these manuscripts, i.e. brought the Mayan hieroglyphs to the same shape. Then, scientists encoded these hieroglyphs and included into the computer memory. In addition, the images of the Mayan manuscripts were also encoded and included into the computer memory. The goal here was not to find the meaning of separate Mayan hieroglyphs, but the whole meaning of parts of the text. It is known that the definition of the frequency of allographs and syllables plays an important role when reading the manuscripts. In other words, for deciphering the Mayan manuscripts, the prevalence rate of different letter combinations (graphemes) must be determined, and then, the frequencies Mayan hieroglyphs should be calculated and obtained prevalence frequency should be compared. As the result of the comparison of the frequencies obtained based on the developed program, the meanings of the hieroglyphs of the Mayan manuscripts have been found. Thus, the ancient Mayan manuscripts have been decoded through the computer - one of the greatest achievements of cybernetics. When exploring the scope of PC, inevitably, the question arises: is it possible to create artificial intelligence?

www.jpis.az 97 Problems of information society , 2016, №2, 91-100

Before creating the artificial intelligence, that is, an “intelligent” machine “thinking” and “understanding” as a human, it is necessary to explore the essence of the human himself/herself. It should be noted that internal and external devices of the computer need to be improved most. These devices are unable to read the hand-written texts directly and to speak as a man yet. Though, any scanned text is now possible to be included into the computer memory. However, since the recognition programs are limited, it is impossible to read arbitrary texts. Bilateral relations through Skype program can be built over the Internet. Nevertheless, the problem of speech recognition to handle the computer by natural language still remains unsolved. Extensive studies should be conducted on phonology, lexicology, syntax, grammar, semantics, and morphology on various language levels in order to teach the machine to talk to a man. Obviously, sounds is the spread of different frequency waves in the air. Different people may differently sound the same word in own way. Apparently, each individual’s voice tone, timbre, intonation and so on differ. For this reason, it is very difficult for the computer to recognize the voice. Nevertheless, the human voice can be artificially synthesized by conversing the sound dances into the electronic ones. For the voice recognition, the voice is filtered and then compared to the etalon stored in computer memory. If any match occurs, the machine identifies that voice, otherwise it fails. Another method is based on the recognition of separate sounds, syllables and words. When it comes to the computer “vision”, each letter is coded and included into the computer’s memory. Thus, the characters included into the computer's memory will be recognized only. Thus, the creation of the “vision” and “conscious” computer does not mean the formation of the artificial intelligence yet. Intelligent computer that can be considered at that time, introduced him to solve a mathematical problem or a task in itself may pose. In fact, there is no a general idea about the notion “conscious”. Usually, the "conscious" means the creative ability or in other word, the ability to solve any problem differently from the previous experiences but with the vision of the interaction between the previous outlook and the surrounding objects. Actually, thinking is a broader concept, which combines a number of features as the perception of abstract and super abstract senses, teaching ability, character formation, decision-making and so forth. A group of experts working in this field believes that the whole nature of the human thinking can be applied in the process of data processing. Therefore, these processes can be programmed in high performance computers and used. Thus, this computer, in principle, may be as a human in all spheres of intellectual activity. In-depth research work has already started for the creation of artificial intelligence. Conclusion Thus, the application of mathematical methods in linguistics is not irrational; it is needed to address certain issues. Universal programming languages (algorithmic languages) have been developed in order to implement the automated programming of the given problem. Due to these languages, the issues facing not only the natural languages, but also a number of humanitarian issues, including automated texts analysis in linguistics, machine translation, dictionary compiling can be programmed and solved. It should also be noted that the recently published “Computer Linguistics” work by Mahmudov M. dedicated to the theoretical and experimental issues of linguistics has led to the resonance in the scientific society [42]. This monograph provides the comprehensive explanation of the national corpus of the Azerbaijani language, statistic lexicography, machine translation and formal linguistic analysis systems. Linguistic algorithms of explored formal analysis systems is of great importance in terms of language emergence and development. They can be widely used for retrieval systems, automated synthesis and analysis systems. Compiling the frequency

98 www.jpis.az

Problems of information society, 2016, №2, 91-100 and reverse dictionaries of the Azerbaijani language through computer, and statistical analysis of the historical monuments (“The Book of Dede Korkut” and Fuzuli’s language) can provide useful facts to the linguistic studies. Mahmudov M. rightly notes: “Computational Linguistics is a recently emerging science but with a great future, which will benefit the humanity. As it is not possible to imagine the future development of the society without computer, the future development trends and prospects of linguistics are also impossible without computational linguistics” [42]. References 1. Toldova S.Y., Lyashevskaya O.N. Modern problems and tendencies of computational linguistics. 24th International Conference on Computational Linguistics COLING 2012, Mumbai // Linguistics Problems, 2014, No 1, pp. 120-145. 2. http://www.wikipedia.org 3. Shtindlova J. Application of mechanization and automation methods in lexicological study abroad. Automation in linguistics. Moscow, 1966, p. 240. 4. Pines V.Y., Mahmudov M.A. Electronic computing machines in linguistics, B., 1977, p.63. 5. Zubov A.V. Processing of natural language text in "man-machine" system. Speech Statistics and automated text analysis, Moscow, 1971, c. 286-434.4. VY Pines, Mahmudov MA Electronic counting machines, linguistics, B., 1977, p. 63. 6. Valiyeva K.A., Mahmudov M.A., Pines V.Y., Rakhmanov S. Frequency dictionary of the Azerbaijani newspaper language, B., 1997, p. 212. 7. Valiyeva K.A., Mahmudov M.A., Pines V.Y., Rakhmanov S. Frequency dictionary of the Azerbaijani newspaper language, B., 2004, p. 264. 8. Mammadov M.A., Fatullayev A., Mammadova S. et al. Frequency dictionary of the Azerbaijani language (word roots), B., 2010, Vol.I, p.464. 9. Methods of Mathematical Statistics and Modelling in the comparative history of linguistics, http: //www.yazıkoznanie.ru/content/view/127/215. 10. Valiyeva K.A., Mahmudov M.A., Pines V.Y. and et al. Statistical analysis of "The Book of Dede Korkut", B., 1999, p. 248. 11. Mahmudov M.A., Fatullayev A. Reverse-Dictionary of the Azerbaijani language, B., 2004, p. 258. 12. Valiyeva K.A. Automated text analysis and synthesis, B., 1996, p.158. 13. The Modern Azerbaijani language. B., 1981, p. 443. 14. Mirzaliyeva M.M. Phraseology of the Turkic languages. B., 2009, p.240 15. Garayeva M.S. Mathematical linguistics in education. (Justification of the problem of foreign language learning by model). Baku, 1964, p.55. 16. Akhundov A.A. Mathematical linguistics. B., 1979, p.79 17. Akhundov A.A. Selected works. Vol.II, B., 2012, p.464 18. Valiyev A.H. Passage accents of the Azerbaijani language. Doctoral thesis. B., 1974, p.400 19. Valiyev A.A. Passage accents of the Azerbaijani language. B., 2005, p.334 20. Melnikov G.P. Some methods of description and analysis of the vowel harmony in the modern Turkic languages // 1962, 6, pp.31-58. 21. Pines V.Y. Modeling of the structure of the Azerbaijani verb forms in connection with the problem of automated dictionary. 1970, p.19. 22. Mahmudov M.A. Development of the system of formal morphological analysis of the Turkic word forms (based on the Azerbaijani language), Baku, "Elm", 1982, p.26.

www.jpis.az 99 Problems of information society , 2016, №2, 91-100

23. Amirov Z.M. Development of the formal models of the Azerbaijani language and establishment of linguistic processor based on them. Abstract of the thesis submitted for the scientific degree of PhD in technical sciences, B., 2006, p.19 24. Fatullayev A.B. Development and application of digit-modeling method of the Azerbaijani-English machine translation system. NDA, B., 2006, p.19 25. Khalili A.M. Development of formal grammar of the “Limited Azerbaijani language” as part of the knowledge base of the deductive machine, NDA, B., 2009, p.34 26. Kulieva Z.Y. Determination of the optimal structure of the automated dictionary and machine translation systems. B., 2011, p.46. 27. Iskandarova N.A. Software-frequency and quality-semantic analysis of the translation adequacy and its application to the translations of different languages. 2008, p.19. 28. Melnikov G.P. Systemology and linguistic aspects of cybernetics. Moscow, 1978, p.368. 29. Valiyeva K.A. A formal description of the synthesis of words, Moscow, 1971, p.20. 30. Valiyev A.G. Transitional dialects of the Azerbaijani language. ADD. B., 1975, p.65. 31. Mahmudov M.A. Automated processing system of the Azerbaijani texts, DDA, B., 1994, p.64 32. Valiyeva K.A., Mirzaliyeva M.M. Azerbaijani-English translation system (theoretical problems) Manuscript, 2015, p.245 33. Valiyeva K.A., Mammadova M.H. Automated texts editing. B., 2003, p.80 34. Valiyeva K.A., Mahmudov M.A. Linguo-statistics: Speculation and reality. B., Studies 1, 2000, pp. 56-63. 35. Valiyeva K.A., Mahmudov M.A. Pines V.Y. and et al. The statistical analysis of «The Book of Dede Korkut" (preliminary results), B., 1999, p.248 36. Valiyeva K.A., Mahmudov M.A., Sultanov V.S. alphabet-frequency glossary of Fuzuli’s poetry. B., 2004, p.548 37. Rakhmanov J.F. Statistical and distributive analysis of the Azerbaijani text (at graphemes and phonemes level). Abstract of PhD thesis. B., 1988, p.23. 38. Mammadova M.H. Automated selection of lexicon in the information - retrival thesaurus based on an analysis of the terminological dictionaries. Abstract of PhD thesis. M., 1984, p.20, 39. Mammadova M.H. Development of the terminological databank of the Azerbaijani language // Soviet Turkology, 1990, No2, pp.84-89. 40. Shikhiyeva I.K. Linguo-statistic features of The Book of Dede Korkut. Abstract of PhD thesis, B., 1995, p.12 41. Popov E.V. Artificial intelligence. Book. 1, Communication systems and expert systems. M., 1990, p.464 42. Veysalli F.Y. Elements of general and private linguistics. I part. B., 2011, p.357 43. The Great Soviet Encyclopedia, Vol. 26, Second Edition, 1954, p.652

100 www.jpis.az

Problems of informationABOUT society AUTHORS, 2016, №2, 91-100

Rasim Mahammad Alguliyev Roza Ordukhan Shahverdiyeva Academician, Director of the Institute of Senior research fellow of the Institute of Information Technology of ANAS Information Technology of ANAS

Rasim Sharif Mahmudov Zarifa Qasım Cabrayılova Sector chief of the Institute of Information PhD, leading research fellow of the Institute Technology of ANAS of Information Technology of ANAS

Yadigar Nasib Imamverdiyev PhD, head of department of the Institute of Afruz Mukhtar Gurbanova Information Technology of ANAS Sector chief of the Institute of Information Technology of ANAS Rashid Gurbanali Alakbarov PhD, technical director of the Institute of Information Technology of ANAS Ramiz Huseyn Shikhaliyev PhD , Head of sector of the Institute of Makrufa Sharif Hajirahimova Information Technology of ANAS PhD, chief project engineer of the Institute of Information Technology of ANAS Kamala Kamil Hashimova Aybeniz Salman Aliyeva Sector chief of the Institute of Information Senior research fellow of the Institute of Technology of ANAS Information Technology of ANAS

Alovsat Garaja Aliyev Kamila Abdulla Valiyeva PhD, associate professor, head of Doctor of philological sciences, head of the department of the Institute of Information department of the Institute of Linguistics Technology of ANAS named after Nasimi of ANAS

www.jpis.az 101 Requirements on design of articles published in the journal

Articles published in the paper, as well as in electronic form will be submitted.

The following requirements must be taken into account while designing the article:

1. Articles should be prepared in one of the languages - Azerbaijani, Russian and English, the title of the article, abstract and keywords should be submitted. 2. The articles should be submitted in the Microsoft Word text editor A4 format (from left - 2 cm., above, below and right - 2.5 cm.), Times New Roman 12 font., 1 cm inter-text interval, remaining paragraphs and not exceeding 12 pages. 3. Article text consisting the following sections is recommended: • Introduction (the problem relevance, state-of-the-art of the problem) • The purpose of the research, statement of the problem • The problem solution methods and approbation • Application of achieved results • Conclusion 4. The articles must be prepared in the following sequence: UOT - from left, bold, 6 font. interval; initials and surname of authors - from the left, and bold italic; author’s place of work, city, country and e-mail address - from the left, in the end 6 font, interval; the title of the article - from the middle, capitalized, bold, 6 font, interval; abstract (in the language of the article) - italics, in the end 6 font, interval; interval; keywords - italics, in the end 6 font, interval; introduction and other sub-headings - from left, bold, and at the beginning and end 6 font, interval. 5. References: each referred source must be numbered in accordance with the sequence used in the article and remained untranslated. 6. References must be followed by the article title, abstract and key words in 2 specified languages. 7. The tables and pictures in the article must be numbered: Table – at the top of the table, from the right (eg, Table 1.), a picture - below the picture, from the middle (eg, Picture 1.) remaining parts (from the above and below) a blank line. 8. Formulas must be set in standard parameters - Microsoft Equation. Only formulas used in the text must be numbered. The formula numbers must be written in brackets in the right. 9. The articles submitted to the editorial board must contain information about the authors: first name, last name, middle name, scientific degree, scientific rank, place of work, position, phone number, or e-mail address. 10. The author is responsible for the information and facts mentioned in the article. 11. The articles received by the editorial office are presented for review and the articles with positive reviews are recommended for publication. 12. Editorial Board Address: AZ 1141, Baku city., B.Vahabzadeh str.,9. Institute of Information Technology, ANAS, "Problems of Information Technologies" journal editorial board. E-mail: [email protected]

Technical editor: Anar Samidov Correctors: Sabina Mammadzade Computer design: Gulnar Cabarli Konul Valiyeva

The journal was registered at the Ministry of Justice of the Republic of Azerbaijan on February 11, 2010. Registration number of adoption into publication:3104 Signed for publication 22.07.2016. Printed pages 60x84. Order №14. Copies 500 102 www.jpis.az