WaaS: Wisdom as a Service
Jianhui Chen, Tsinghua University
Jianhua Ma, Hosei University
Ning Zhong, Maebashi Institute of Technology and Beijing University of Technology
Yiyu Yao, University of Regina
Jiming Liu, Hong Kong Baptist University
Runhe Huang, Hosei University
Wenbin Li, Shijiazhuang University of Economics
Recent advances in Zhisheng Huang, Vrije University Amsterdam
cloud computing, the Yang Gao, Nanjing University
Internet of Things, Jianping Cao, National University of Defense Technology
the Web of Things, huge number of sensors, embedded appliances, and actuators have Big Data, and other A been deployed in almost every part of every major city. The Internet, research fields have the mobile Internet (MI), the Internet of Things (IoT), and the Web of Things created the chance (WoT) connect humans, computers, and other devices to form an immense to develop an open network by which various information tech- cyber world and accessed computers and architecture with nologies (IT) and their applications per- networks only when we needed IT ser- meate into every aspect of our daily lives. vices. The hyper-world changes this kind intelligent sharing These continuously extending IT applica- of loose coupling between humans and tions have resulted in a hyper-world consist- computers. Owing to the fusion of the so- and services. ing of the social, cyber, and physical worlds cial, cyber, and physical worlds, today we and using data as a bridge.1,2 live within a huge network of numerous In the hyper-world, the most important computing devices, storage devices, and changes and characteristics include the following: u-Things, where real physical objects are attached, embedded, or blended with com- • The human-computer relationship. In puters, networks, or some other devices the past, we lived separately from the such as sensors. Adapting and utilizing this
40 1541-1672/14/$31.00 © 2014 IEEE IEEE INTELLIGENT SYSTEMS Published by the IEEE Computer Society Agents Companies/Societies Sociocultural & organizational kind of new human-computer re- components lationship is an urgent task for the Individuals Social world development of IT applications. Human • Big Data. The extension of the Services Internet, IoT, and MI greatly ac- Wisdom celerates the production of data. W2T cycle in the hyper-world IDC (a technology research firm) Knowledge estimates that data has been con- Information stantly growing at a 50 percent Cyber world increase each year, more than dou- Data bling every two years. Big Data is Things: The Internet/Web of Things an important characteristic of the hyper-world, so how to effectively Physical world manage, mine, and utilize it to im- prove the ability and quality of IT applications is an urgent task.3,4 Figure 1. The Wisdom Web of Things (W2T) cycle in the hyper-world. Essentially, it These changes have led to the ap- loops from things to data, information, knowledge, wisdom, services, humans, and pearance of many new phenomena then back to things. and research issues. For example, in China, there’s a phenomenon called of the Wisdom Web of Things (W2T), storage and computing resources Human Flesh Search Engine (HFSE), where the “wisdom” means that each needed by Big Data can also be ob- in which a large number of Web us- of the “things” in IoT and WoT is tained by each IT application to im- ers voluntarily gather together to col- aware of both itself and others to pro- prove its ability and quality. laborate and conduct truth-finding vide the right service for the right ob- However, cloud computing primar- tasks, mostly without monetary re- ject at the right time and context.2 ily focuses on the system resource ar- ward. The organizational structure The W2T core concept is a processing chitecture of IT applications—that and incentives that motivate people cycle, namely, “from things to data, is, infrastructures, platforms, and to contribute shed light on the in- information, knowledge, wisdom, software (developing and schedul- trinsic understanding of voluntary, services, humans, and then back to ing abilities). For the large scale con- large-scale crowdsourcing and how things” (the W2T cycle, for short), verging of intelligent IT applications, collective intelligence is fulfilled with as shown in Figure 1. Similar to the it’s necessary to develop an open and the help of the Internet.5 The hyper- real-world material cycle that ensures interoperable intelligence service ar- world is bringing profound influences the harmonious symbiosis of animal, chitecture for the contents of IT ap- in both work and life to society. How plant, and microbes, the W2T cycle plications—the data, information, to realize the organic amalgamation realizes the harmonious symbiosis of knowledge, and wisdom (DIKW). and harmonious symbiosis among hu- humans, computers, and things in the The hyper-world with its W2T cycle mans, computers, and things in the hyper-world. will serve this purpose. hyper-world is becoming a significant Constructing the W2T cycle re- scientific and social issue. lies on the large-scale converging of From Data to Wisdom intelligent IT applications. Cloud From Wisdom Web to computing provides an open and ser- Where is the life we have lost in living? Wisdom Web of Things vice-oriented architecture for meeting Where is the wisdom we have lost in Web intelligence (WI) may be viewed such a challenge.9 It’s a new trend in knowledge? as an enhancement or extension of ar- the IT industry with the potential to Where is the knowledge we have lost in tificial intelligence (AI) and IT on the realize a pay-as-you-go model. Based information? Web.6 One of its goals can be defined on cloud computing, all IT applica- —T.S. Eliot, “The Rock,” 1934 as the development of a wisdom Web.7,8 tions can be deployed on a uniform In the hyper-world age, this goal platform and organized flexibly for The first step in developing a con- can be extended to the development varied applications. The enormous tent architecture of IT applications is november/december 2014 www.computer.org/intelligent 41 Cloud Computing
Interaction, personalization, context- WaaS architecture aware, affective/emotion, auto- DIKW hierarchy perception, active-service, … Wisdom Wisdom-as-a-Service Ontologies, models, rules, … (WaaS) current and future information. Pro- viding current information is part of Metadata, data features, Knowledge Knowledge-as-a-Service refined records, cases, ... (KaaS) the activity of getting information Information from diversified resources, informa- Webpages Texts Videos Information-as-a-Service tion retrieval being the most typical. (InaaS) Data Providing future information means ... Audio Pictures Data-as-a-Service extracting information from data Data structuring, relational connection, distillation or pattern recognition (DaaS) per user demand. Related services Big Data Collecting and understanding the information Transforming outside knowledge to the inner include data mining services and and judicious application of knowledge data curation services.3 • Knowledge as a service (KaaS) pro- Figure 2. From the data, information, knowledge, and wisdom (DIKW) hierarchy to vides services with respect to ex- wisdom as a service (WaaS). isting and will-be-refined explicit knowledge, such as formal ontolo- to investigate the relationships among Because of the fusion of humans, com- gies, user models, and so on. Pro- contents. As suggested in T.S. Eliot’s puters, and things in the hyper-world, viding existing knowledge is part of poetic lines, a common understand- developing human-level intelligence be- what knowledge query services do; ing of the DIKW hierarchy10 would comes a tangible goal of DIKW-related providing will-be-refined knowledge include the following four levels: research. Realizing it will depend on a means to construct new knowledge holistic intelligence research.2 bases according to user demand. • Data is the uninterpreted raw Related work concerns knowledge quantities, characters, or symbols Wisdom as a Service retrieval,11 the development and collected, stored, and transmitted. Architecture management of knowledge bases. • Information is a collection of in- Based on the DIKW hierarchy, we pro- • Wisdom as a service (Waas) pro- terpreted, structured, or organized pose a wisdom as a service (WaaS) ar- vides various intelligent IT appli- data that’s meaningful and useful chitecture of IT applications as shown cations, including software and for certain applications. in Figure 2. We have two senses of u-Things, as “wisdom” services. Intel- • Knowledge is acquaintance or fa- WaaS. In a wide sense, we use WaaS ligent technologies are core to WaaS miliarity about facts, truths, or to denote a new content architecture and involve personalization, context principles gained through study or of IT applications which includes four awareness, affective/emotion, interac- investigation. service layers; in a narrow sense, we tion, autoperception, active services, • Wisdom is sagacity, discernment, use WaaS to denote the highest ser- and so on. By using these intelligent or insight to know what’s true vice layer provided by the WaaS ar- technologies on data, information, or right for making correct judg- chitecture. More specifically, the four and knowledge, those software and ments, decisions, and actions. service layers in the Waas architecture u-Things can make correct judg- can be described as follows: ments, decisions, and actions to pro- Each level in this hierarchy can affect vide the right service for the right the other and be changed into another: • Data as a service (DaaS) provides object at a right time and context. information can be obtained from data services based on both current and by data structuring, relational connec- future raw data. Providing current tion, distillation, or pattern recognition; data is part of the data-sharing ser- WaaS Standard and knowledge can be refined by collect- vice realized in all scientific data- Service Platform ing and understanding the information; bases. To realize data sharing, the Figure 3 gives the WaaS standard and wisdom can be realized by trans- data collection service integrates and service platform architecture, forming outside knowledge to the inner dispersive data into a unified data- with four components according to and judicious application of knowl- base. Providing future data is part the four service layers. Each com- edge. Such creation, organization, and of the service that obtains data ac- ponent includes a software platform transformation are the essence of the cording to user demand. and a standard system for realizing DIKW hierarchy and a core research • Information as a service (InaaS) all the services in the corresponding issue of intelligent IT technologies. provides services by using both service layer. The software platform
42 www.computer.org/intelligent IEEE INTELLIGENT SYSTEMS Intelligent IT HFSE standards WaaS application portal application standards WaaS Data-information- Decision- Content schedule standards knowledge bus making helper WaaS standard system WaaS platform
Data accessing Information accessing Knowledge accessing standards standards standards Data content and Metadata standards Knowledge format standards representation and Data-mining-related organization standards Data transmission standards protocols Knowledge retrieval Information-retrieval- standards Data collection related standards interface standards KaaS standard system InaaS standard system DaaS standard system
Data query Data curation Knowledge query Data management Information management Knowledge management Data cleaning Data mining Knowledge development Data collection Information retrieval Knowledge retrieval DaaS platform InaaS platform KaaS platform
DaaS InaaS KaaS
Cloud computing platform
Brain and intelligence Big Data center
Figure 3. WaaS standard and service platform architecture. A group software platform and standard systems for realizing different layers of services in the WaaS architecture. is an open portal on which differ- the Web, information systems, • The InaaS platform includes the in- ent service modules can be deployed, deploying sensors, embedded chips, formation retrieval, data mining, and the standard system is a group of experimental devices, and so on. information management, and data standards and specifications that de- • The DaaS standard system consists curation modules to support the four scribe the requirements and methods of four types of standards, namely, types of InaaS services. Data cura- for developing and using service mod- data collection interface standards, tion focuses on information organiza- ules on the software platform. All the data transmission protocols, data tion, including metadata construction software platforms and standard sys- content and format standards, and and case creation. Because the hyper- tems can be described as follows: data accessing standards. These stan- world includes mutable data, com- dards or protocols are based on ap- puting, and network environments, • The DaaS platform includes the plications and can be classified into it’s necessary to perform the offline data collection, cleaning, manage- different types according to different information extraction and organiza- ment, and query modules to support data sources, application environ- tion before services are requested. the three types of DaaS services. The ments, and application purposes. For • The InaaS standard system con- core is the data collection module, example, different data transmission sists of four types of standards, consisting of many data collection protocols should be designed and namely, information retrieval-related, interfaces for “reading” data from used in the Internet and MI. data mining-related, metadata, and november/december 2014 www.computer.org/intelligent 43 Cloud Computing
Hyper-world Agents Companies/Societies Socio-culture & decision-making helper is used to as- organizational Social world Individuals components sist judgments and decisions. Based
Content architecture ( on them, various intelligent IT appli- ) WaaS (Personalization, context-aware, affective/emotion, cations can be deployed on the WaaS interaction, auto-perception, active-service) application portal to make correct Software-as-a-Service (SaaS) KaaS actions—namely, to provide intel- (Applications, workflows) (Ontologies, models, rules) Cyber world WaaS ligent services. Furthermore, HFSE Platform-as-a-Service (PaaS) InaaS ) (Middleware application server, web server) (Metadata, data features, cases) can also be regarded as a kind of spe-
System architecture (cloud computing IaaS Data-as-a-Service (DaaS) cial intelligent applications. (Servers, storage devices, internet/IoT/MI*) (Big Data—webpages, videos, texts, pictures) • The WaaS standard system consists of content (data, information, and knowledge) schedule standards, in- Physical world telligent IT application standards, and HFSE standards. They concern content schedule languages, appli- Figure 4. An open and interoperable architecture of IT applications for Wisdom cation interfaces, application wrap- Web of Things (W2T). It binds WaaS and cloud computing to meet six core demands pings, application communications, of intelligent IT applications, including infrastructures, platforms, software, data, information, and knowledge, in a unified, pay-as-you-go manner. and HFSE-related technical stan- dards and laws.
All software platforms are open. information accessing standards. module are used to manage formal Based on the standards and protocols Information retrieval-related stan- knowledge and provide knowledge in the four standard systems, any third dards mainly focus on application sharing services, respectively. party can develop and plug in their service definition and protocol speci- • The KaaS standard system con- service modules on these platforms, fication, whereas data mining-related sists of knowledge retrieval-related which are also interoperable. Be- standards are involved with mining standards, knowledge representa- cause they adopt the same standards languages, result representation lan- tion and organization standards, and protocols, different service mod- guages, mining system architectures, and knowledge accessing standards. ules on platforms can effectively com- and so on. Metadata standards are The knowledge retrieval-related municate and cooperate with each classified and defined according to standards are mainly used to de- other to provide the various services application domains. Information ac- fine application services and proto- in the DIKW hierarchy. Furthermore, cessing standards are used to define cols. The knowledge representation other systems and platforms can also accessing interfaces and transmission and organization standards focus cooperate with these service modules protocols of InaaS service modules on on knowledge representation lan- based on the above standards and the InaaS platform. guages, such as the cyber-individual protocols. This openness and interop- • The KaaS platform includes the (Cyber-I) representation language.12 erability make it possible to converge knowledge retrieval, development, Knowledge accessing standards all technologies and resources into a management, and query mod- mainly involve knowledge query unifying framework for realizing a ules for various KaaS services. The languages, accessing interfaces, sys- W2T cycle. knowledge retrieval module is devel- tem architectures, transmission pro- oped based on WI-specific technol- tocols, and so on. Binding WaaS with ogy that extracts knowledge from • The WaaS platform includes the Cloud Computing enormous information sources. The data-information-knowledge bus, The development of intelligent IT ap- knowledge development module decision-making helper, and WaaS plications needs to meet both system- provides various tools for the de- application portal. The data-infor- and content-level demands. Typically, velopment of ontologies, models, mation-knowledge bus is a spe- system-level demands are related to and other types of formal knowl- cial enterprise service bus (ESB) infrastructures (network, storage, edge. The knowledge management for discovery and integration of and computing resources), running module and knowledge query DaaS, InaaS, and KaaS services. The platforms, and software (developing
44 www.computer.org/intelligent IEEE INTELLIGENT SYSTEMS D: Data I: Information K: Knowledge W: Wisdom WaaS standard and service platform
WSDL/SOAP/UDDI Wisdom service platform WaaS BPEL-based content schedule Wisdom mobile application languages D D-I-K-W W WaaS standard system WaaS platform Doctors Analysts K D-I D-I I ASTM E1467-92 WSDL/SOAP/UDDI OWL/RDF JSON Data provenance SWRL UsersU schema SPARQL D WSDL/SOAP/UDDI/ Analysis provenance WaaS standard and D-I-K schema JSON service platform DaaS standard system InaaS standard system WSDL/SOAP/UDDI
Brain and intelligence KaaS standard system Big Data center Cloud computing Cyber-I platform Data downloading interface aData query interface Cyber-I tool Non-real-time data BI provenances Cyber-I query interface collection interface toolbox ESB (SaaS) Cloud computing Real-time data Parallel data mining LarKC collection interface toolbox Platform software (PaaS) platform DaaS Platform InaaS platform KaaS platform Virtualization software (IaaS) DaaS InaaS KaaS
IInternetnternet RouterRouter FirewallFirewall Branchc exchange Computing servers LAN RAID
Figure 5. Technological framework of the portable brain and mental health-monitoring system. Various system- and content- level demands in developing this intelligent IT application are met by realizing the architecture that binds WaaS with cloud computing. and scheduling abilities), whereas Case Study demands. As Figure 5 shows, various content-level demands are related to Based on the architecture that binds servers, a redundant array of indepen- Big Data and its processing. WaaS with cloud computing, we de- dent disks, mobile phones, local area Figure 4 shows an open and interop- veloped a prototype of a portable networks, the Internet, and MI form erable architecture of IT applications brain and mental health-monitoring a powerful infrastructure on which that binds WaaS and cloud comput- system (brain-monitoring system, for virtualization software is stepped up ing. It meets two types of demands short) to support the monitoring of to dynamically provide needed in- in a unified, pay-as-you-go manner: brain and mental disorders, a huge frastructure resources as services for cloud computing provides an open public health concern. As Figure 5 the brain-monitoring system based IT architecture for sharing system shows, various system- and content- on the IaaS mode. Open platform resources, including infrastructures, level demands in developing this in- software provide the needed running platforms, and software, by means telligent IT application can be met PaaS mode, and we adopted Web ser- of IaaS, PaaS, and SaaS. Paralleling by integrating cloud computing and vice technologies to develop all the the cloud model, WaaS is about shar- WaaS. functional components and software ing content resources and processing We adopted cloud computing (in- systems as services on the ESB based utilities. Through these “as a service” cluding IaaS, PaaS, SaaS) to meet the on the SaaS mode. layers, six factors—infrastructures, system-level demands of the brain- We used the WaaS architecture platforms, software, data, informa- monitoring setup based on the “any- (DaaS, InaaS, KaaS, WaaS) to meet tion, and knowledge—will converge thing as a service” paradigm. As a the content-level demands of the into an open and interoperable uni- cross-platform, data-intensive in- brain-monitoring system based on the form platform, on which all factors telligent system, it needs credible “anything as a service” paradigm. To can be effectively utilized by various infrastructures, an open running provide smart monitoring services, intelligent technologies to form an in- platform, and an extensible develop- the brain-monitoring system needs telligent service layer—specifically, ing and scheduling mode. We con- high-quality monitoring data, effi- the WaaS layer. structed a private cloud to meet these cient data processing capabilities and november/december 2014 www.computer.org/intelligent 45 Cloud Computing
The Authors
Jianhui Chen is a postdoctoral fellow in the Department of Computer Science and Tech- nology, Tsinghua University. His research interests include Web intelligence, brain infor- mobile phones, global research matics, and knowledge engineering. Chen has a PhD in computer science from Beijing groups and individuals can pro- University of Technology. Contact him at [email protected]. vide data production and sharing Jianhua Ma is a professor in the Faculty of Computer and Information Sciences, Hosei services to collect high-quality mon- University. His research interests include smart worlds and ubiquitous/pervasive intelli- itoring data. gence. Ma has a PhD in information engineering from Xidian University. Contact him at [email protected]. • The InaaS platform is for informa- tion-level demands. Global analysts Ning Zhong (corresponding author) is the head of the Knowledge Information Systems can use the parallel data mining Laboratory and a professor in the Department of Life Science and Informatics, Maebashi Institute of Technology, Japan. He is also the director and an adjunct professor in the toolbox and the brain informat- International Web Intelligence Consortium (WIC) Institute, Beijing University of Tech- ics (BI) provenances toolbox13 to nology. His research interests include Web intelligence, brain informatics, data mining, realize various data mining and granular computing, and intelligent information systems. Zhong has a PhD in the inter- disciplinary course on advanced science and technology from the University of Tokyo. curation services, which provide ef- Contact him at [email protected]. ficient data processing capabilities and methods to extract peculiar Yiyu Yao is a professor in the Department of Computer Science, University of Regina, and International WIC Institute, Beijing University of Technology. His research in- indexes from patients’ EEG data terests include three-way decisions, Web intelligence, and information retrieval. Yao and integrate obtained indexes and has a PhD in computer science from the University of Regina. Contact him at yyao@ other related information into BI cs.uregina.ca. provenances. Jiming Liu is the chair professor in computer science and associate dean of science, Hong • The KaaS platform is for knowl- Kong Baptist University, and is affiliated with International WIC Institute, Beijing Uni- edge-level demands. Based on the versity of Technology. His research interests include Web intelligence, autonomy-oriented computing, and complex systems/networks modeling. Liu has a PhD in electrical engi- LarKC (Large Knowledge Col- neering from McGill University. Contact him at [email protected]. lider),14 the Cyber-I tool and the Cyber-I query interface make it Runhe Huang is a professor in the Faculty of Computer and Information Sciences, Hosei University, Tokyo. Her research interests include AI and its applications, distributed in- possible to realize knowledge ex- telligence computing, Web data mining, cloud computing, and hyper-world modeling. traction and query (sharing) ser- Huang has a PhD in computer science and mathematics from the University of the West vices, which provide individualized of England. Contact her at [email protected]. diagnosis models (a kind of spe- Wenbin Li is the head of the Information Security Lab at Shijiazhuang University of Eco- cial Cyber-I) based on peculiar in- nomics. His research interests include machine learning, artificial neural networks, in- dexes and other information in BI formation security, and Web intelligence. Li has a PhD in computer science from Beijing University of Technology. Contact him at [email protected]. provenances. • The WaaS platform is for wis- Zhisheng Huang is a senior researcher in the Department of Computer Science, Vrije dom-level demands. Personalized University Amsterdam, and International WIC Institute, Beijing University of Technol- ogy. His research interests include AI and multimedia. Huang has a PhD in logics and and wisdom service modules are Computer science from the University of Amsterdam. Contact him at [email protected]. deployed on the wisdom service platform and the wisdom mobile Yang Gao is a professor in the Department of Computer Science, Nanjing University. His research interests include Big Data, machine learning, multiagent systems, and image/ application to provide various re- video processing. Gao has a PhD in computer science from Nanjing University. Contact mote and local smart monitoring him at [email protected]. functions. These functions are real-
Jianping Cao is a PhD candidate in the College of Information Systems and Manage- ized based on intelligent technolo- ment, National University of Defense Technology, China. His research interests include gies and DaaS, InaaS, and KaaS influence analysis and social computing. Contact him at [email protected]. services, which are called by the data-information-knowledge bus.
As Figure 5 shows, each subplat- methods, accurate diagnosis models, • The DaaS platform is for data- form is based on the corresponding and personalized and wisdom ser- level demands. By using data col- standard system. For example, devel- vice modules. All these demands are lection interfaces and the data oping the DaaS platform is based on met by a WaaS standard and service downloading interface, as well as the DaaS standard system, which in- platform that includes the four su- corresponding portable EEG (elec- cludes Web services-related standards platforms shown in Figure 5: troencephalogram) equipment and (Web Service Definition Language,
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