Collective Intelligent Information and Database Systems

Journal of Intelligent & Fuzzy Systems 32 (2017) 1157–1160 1157 DOI:10.3233/JIFS-169115 IOS Press Guest Editorial Collective intelligent information and database systems Ngoc-Thanh Nguyena,c,∗, Manuel Nuńez˜ b and Bogdan Trawinski´ c aDivision of Knowledge and System Engineering for ICT, Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam bFaculty of Computer Science, Complutense University of Madrid, Spain cFaculty of Computer Science and Management, Wroclaw University of Science and Technology, Poland Abstract. Collective intelligence is most often understood as group intelligence which arises on the basis of intelligences of the group members. This paper presents an overview of application of collective intelligence methods in knowledge engineering and in processing collective data. It also introduces papers included in this issue. Keywords: Collective intelligence, intelligent systems, knowledge engineering Collective intelligence is most often understood are based on the collective intelligence aspects and as group intelligence which arises on the basis of which use the computational techniques for solving intelligences of the group members. On the one these problems. Regarding knowledge engineering, hand, Newell [19] defined an intelligent collective computational collective intelligence provides meth- as a social system, which is capable to act, even ods and techniques for determining the knowledge approximately, as a single rational agent. On the of a collective as a whole on the basis of collec- other hand, Levy´ [11] understood collective intel- tive members’ knowledge. The need for processing ligence as an intelligence that emerges from the collective knowledge is quickly increasing because collaboration and competition of many individuals; of the very fast development of Internet, social net- an intelligence that seemingly has a mind of its works and distributed databases. It is obvious that own. These definitions refer to cognitive systems. knowledge originating from autonomous sources for From the computational and artificial intelligence the same subject is very often inconsistent. Therefore, point of view, we can think about a collective as the aspects of inconsistency processing and integra- a set of autonomous units working on some com- tion computing are very important. mon task, for example, a multi-agent system. We can Knowledge engineering [9] plays a relevant role say that the collective is intelligent if it can make in CCI since it is necessary to use its techniques a use of the intelligences of its members for solv- in order to represent individual information. In fact, ing some problem, for example, a decision making there are studies advocating that scientific knowl- problem. Thus Computational Collective Intelligence edge is essentially collective knowledge [23]. In (CCI) should provide computational methods which this line, recent studies state that it is very important that different individuals provide orthogonal, ∗ Corresponding author. Ngoc-Thanh Nguyen, Division of highly unrelated, and possible contradictory knowl- Knowledge and System Engineering for ICT, Faculty of Infor- edge to the collectivity. In other words, “the higher mation Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam. Tel.: +34 48713204139; Fax: +34 48713203884; the inconsistency, the better the quality of collec- E-mail: [email protected]. tive knowledge” [20]. Another relevant interaction 1064-1246/17/$35.00 © 2017 – IOS Press and the authors. All rights reserved 1158 N.-T. Nguyen et al. / Collective intelligent information and database systems with knowledge engineering is ontology matching ficult to perform because, due to their short nature, and integration [27], a fundamental activity in order single tweets do not provide enough information and to collect and process information in CCI. Since novel clustering techniques must be used [13]. Inter- it is well known that this process is both time estingly enough, similar problems with NER appear and resource-consuming, it is necessary to provide in other disciplines, most notably in Medicine where advanced architectures and platforms to reduce these active learning methods are successfully used [3]. In costs [15] and good algorithms and frameworks to fact, clustering methods are widely used to detect efficiently integrate ontologies [16, 21]. communities in social networks, where members Although machine learning and data mining are involved in similar social objects are grouped [32], two independent fields of work, there are frequent although some researchers advocate that the exis- interactions between them [18]. Interestingly enough tence of random factors needs to be taken into account CCI uses machine learning and data mining in its so that statistical fuzzy approaches are more suit- solutions but there are also numerous applications able [12]. of CCI to improve machine learning and data mining Intelligent information systems are very useful in processes. A first issue concerns the representation of areas where vast amounts of heterogenous, and usu- data in machine learning, given the fact that efficient ally unstructured information, must be processed. algorithms strongly rely on a good structure of data. These systems are gaining popularity in Medicine, Actually, the research community organizes contests in particular when used to evaluate the quality of to compare existing approaches and to identify future health care systems [1] and monitor the progress of challenges [7] and, as a result, new highly efficient patients [29]. In the latter case, it is necessary to take methods, beating previous learning methods, are into account the special nature of vital signs so that developed to improve representation learning [30]. the best performance to predict patient conditions can Given the huge amount of data generated by current be obtained when using a fuzzy model. information systems, it is a must to use good machine Intelligent database systems play a role in many learning algorithms to discover knowledge in these different areas. In particular, they can be used in Com- vast datasets. In order to reduce the amount of useful puter Science to improve existing methodologies. data, new techniques take advantage of the improve- This is the case of using databases to help in the repli- ment in performance provided by parallel algorithms cation of experiments in Software Engineering [2, to process distributed data [31]. 6]. This is an area where, again, statistic information Learning from data streams is an area of increasing is frequently used to construct robust methods [10]. importance [14] and new approaches are needed to This is specially relevant if datasets are non-normal. perform data stream mining and, again due to the huge The next kind of systems that we consider within amount of processed data, classify the received data, the topics covered in this special issue are decision according to similarity, with good performance [17, support systems. They are particular relevant in the 22]. Data stream mining uses prediction models to context of CCI if we consider them in the scope of deal with historical data. However, these models lose decision making and knowledge engineering, most accuracy if they do not frequently update old data notably, in the context of decisional DNA [25], a with current data, so that it is necessary to deal with structure suited to obtain knowledge in decision mak- concept drift and adapt to it [5]. In particular, in order ing processes, and virtual engineering objects [26]. to cope with this problem when classifying streamed We finish this brief overview of the field with data, new methods must take into account temporal the application of computer vision techniques to dependencies and splitting criteria based on misclas- video surveillance and object detection. This is a sifications [24, 33]. line of work where huge amounts of data must be Another area of interest, highly related to the issue adequately processes and analyzed. Since data are of processing huge amounts of unstructured data, observed and/or collected from distributed locations is text processing and information retrieval. This and intelligent decisions must emerge, in particular line of research is currently very active because of when an imminent danger is recognized, this field the ubiquity of social networks, an obvious area of is in the scope of CCI. The number of cameras interest for CCI. Information extraction in tweets is installed in public areas is steadily increasing with specially challenging because traditional natural lan- the consequent increase of the amount of images guage algorithms cannot be used to process them. In to be processed. One of the current concerns is particular, named entities recognition (NER) is dif- the detection of abandoned objects because of the N.-T. Nguyen et al. / Collective intelligent information and database systems 1159 threat that they can represent. Therefore, new robust and dedication have been fundamental for the suc- and efficient algorithms are being developed, taking cess of this special issue. The list of members can into account varying circumstances such as lighting be found at https://sites.google.com/site/sejifs2016/ changes [28]. Similarly, the detection of pedestrians, guest-editorial-board. Finally, we would like to thank as well as the objects that they carry, is an active Van Du Nguyen (Wroclaw University of Technology) line of work [4, 8] where probabilistic approaches

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