1. Industrial Ontologies Group Activities of Distributed Systems Research Group “Device” “Expert” “Service” Resource Agent PI GB SC Vagan Terziyan vagan@it.jyu.fi

2. Distributed Systems Group in Jyvaskyla Peer-to-Peer Research Group Prof. Jarkko Vuori Industrial Ontologies Group Prof. Vagan Terziyan Distributed Systems Research Group (united) P2P, MP2P resource discovery in dynamic and static networks, with static and dynamic routing strategies, with cooperative and self-interested nodes, with parametrical and structural self-configurability (topology management), with embedded intelligence and learning abilities, etc. Semantic and agent technologies in distributed systems (see next slide)

3. Industrial Ontologies Group http://www.cs.jyu.fi/ai/OntoGroup/  Semantic Web and Ontologies  Web Services and Semantic Web Services  (Multi) Agent Technologies  Distributed Artificial Intelligence  Knowledge Management  Mobile Context-Aware Services and Applications  Machine Learning  Data Mining and Knowledge Discovery G ROUP P ROFILE : The main objective of the group is to contribute to fast adoption of Semantic Web and related technologies to local and global industries. It includes research and development aimed to design a Global Understanding Environment as next generation of Web-based platforms by making heterogeneous industrial resources (files, documents, services, devices, business processes, systems, organizations, human experts, etc.) web-accessible, proactive and cooperative in a sense that they will be able to automatically plan own behavior, monitor and correct own state, communicate and negotiate among themselves depending on their role in a business process, utilize remote experts, Web-services, software agents and various Web applications.

4. IOG cooperates with different units of Jyvaskyla University and leads the activities in the domain “Industrial Applications of Semantic Web” in Finland MIT Department TITU Agora Center Adaptive Services Grid Integrated Project supported by the European Commission  Anton Naumenko  Sergiy Nikitin Proactive Self-Maintained Resources in Semantic Web SmartResource: TEKES TEKES project: ”Industrial Applications of Semantic Web” ” Industrial Applications of Semantic Web” International IFIP Conference on PhD theses  Andriy Zharko  Oleksiy Khriyenko  Anton Naumenko  Sergiy Nikitin  Dmytro Zhovtobrukh  Natalya Kohvakko Courses:  Semantic Web and Web Services  Agent Technologies in Mobile Environment InBCT InBCT project: Semantic Search Facilitator ”Semantic Google”  " IdeaMentoring: Refining research ideas to the new business opportunities" Nokia Nokia projects:  " IdeaMentoring II "

5. Two alternative trends of Web development Human Communities Machines, devices, software, etc Facilitates Human-to-Human interaction Facilitates Machine- to-Machine interaction

6. GUN Concept GUN – Global Understanding eNvironment GUN = Global Environment + Global Understanding = Proactive Self-Managed Semantic Web of Things = ( we believe ) = “Killer Application” for Semantic Web Technology

7. GUN Motivation Growing complexity of computer systems and networks used in industry  need for new approaches to manage and control them IBM vision: Autonomic computing – Self-Management (includes self-configuration, self-optimization, self- protection, self-healing) Ubiquitous computing, “Internet of Things”  huge numbers of heterogeneous devices are interconnected “ nightmare of pervasive computing ” when almost impossible to centrally manage the complexity of interactions, neither even to anticipate and design it. We believe that self-manageability of a complex system requires its components to be autonomous themselves, i.e. be realised as agents. Agent-based approach to SE is also considered to be facilitating the design of complex systems

8. GUN and Ubiquitous Society Human-to-Human Human-to-Machine Machine-to-Human Machine-to-Machine Agent-to-Agent GUN can be considered as a kind of Ubiquitous Eco- System for Ubiquitous Society – the world in which people and other intelligent entities (ubiquitous devices, agents, etc) “live” together and have equal opportunities (specified by policies) in mutual understanding, mutual service provisioning and mutual usability.

9. GUN-GERI-UBIWARE-SmartResource ? GUN (Global Understanding Environment) – Proactive Self-Managed Semantic Web of Things - general concept and final destination GERI (Global Enterprise Resource Integration) – GUN subset related to industrial domains UBIWARE – middleware for GERI SmartResource – semantic technology, pilot tools and standards for UBIWARE

10. Core DAI platform Adaptation and personalization Proactivity and behavior Coordination and networking Autonomicity, self- management, self- configurability Metadata, semantics, ontologies Security and trust Human- centricity, GUI, Web 2.0, Wiki Industrial cases implementation Intelligence, learning, reasoning, planning … The Roadmap towards GUN Qualitative transitions

11. WIDER OBJECTIVE - to combine the emerging Semantic Web, Web Services, Peer-to-Peer, Machine Learning, Ubiquitous Intelligence and Agent technologies for the development of a global GUN-based EAI Platform and smart e-maintenance environment, to provide Web-based support for the predictive maintenance of industrial devices by utilizing heterogeneous and interoperable Web resources, services and human experts Project results in the Web: http://www.cs.jyu.fi/ai/OntoGroup/SmartResource_details.htmhttp://www.cs.jyu.fi/ai/OntoGroup/SmartResource_details.htm

12. SC Challenge 1: General Adaptation Framework Universal reusable semantically-configurable adapters

13. GB Challenge 2: General Proactivity Framework Role “Feeder” descriptio n Role “SCADA” descriptio n Role “Maintenanc e worker” description Universal reusable semantically-configurable behaviors

14. PI Challenge 3: General Networking Framework Scenario “Predictive maintenance” description Scenario “Predictive maintenance” description Universal reusable semantically-configurable scenarios for business processes Scenario “Data integration” description Scenario “Data integration” description

15. SmartResource project summary SmartResource Tekes project (2004-2006): http://www.cs.jyu.fi/ai/OntoGroup/projects.htm. http://www.cs.jyu.fi/ai/OntoGroup/projects.htm One of the most essential results of the SmartResource project was creation of the “Smart Resource Technology” for designing complex software systems. The technology benefits from considering each traditional system component as a “smart resource”, i.e. proactive, agent-driven, self- managing.

16. What is next?

17. UBIWARE: “Smart Semantic Middleware for Ubiquitous Computing” Submitted to Tekes (final update 27 February); In the Web: http://www.cs.jyu.fi/ai/Application_UBIWARE_2007.doc http://www.cs.jyu.fi/ai/Application_UBIWARE_2007.doc http://www.cs.jyu.fi/ai/Application_UBIWARE.ppt Starts if accepted: 1 May 2007; Summary:  UBIWARE project will be build on the foundation laid in the SmartResource project. SmartResource analyzed the central concepts related to our GUN vision, and resulted in some pilot tools and solutions. In turn, UBIWARE will result in a complete and self-sufficient middleware platform. In addition to treating the central issues related to flexible semantic agent-based integration and coordination of heterogeneous components, it will develop appropriate solutions in supporting but mandatory areas such as security, human interfaces and other. Partners:  IOG (AC, UJ), ABB, Fingrid, Hansa Ecura, Inno-W, Metso Automation, Metso Shared Services, TeliaSonera

18. GERI: “Global Resource Integration in a Networked Enterprise” EU FP7 STREP proposal for the objective ICT-2007-1.3: “ICT in support of the networked enterprise” ; Deadline for submission: 8 May 2007; Summary:  The traditional concept of Enterprise Application Integration (EAI) must now be extended towards Global Enterprise Resource Integration (GERI) that aims at effective and seamless integration of all different types of resources found in an enterprise: digital, physical, and human. Project intends: to develop architecture for seamless integration of information resources (e.g. software applications, web services) with sensor and RFID devices based on the agents technology leading to a more homogeneous environment; to develop tools and methodology for development of distributed systems through integration and reuse of existing heterogeneous components (software or devices), through declarative integration, where the components and their interaction are defined and configured declaratively (semantically) rather than programmatically; to elaborate business models of inter-enterprise collaboration through GERI-based environments; to analyze application of the GERI approach in Distribution Automation business integration scenarios. Partners: IOG, Tel Aviv University, Free University of Amsterdam, several SMEs

19. SWIMMER: emantic eb ntegrator SWIMMER: Semantic Web Integrator for aintenance anagement of for Maintenance Management of nterprise esources Enterprise Resources SWIMMER project (started at December 2006) appeared as a result of cooperation of Industrial Ontologies Group and Metso Automation in SmartResource project. Upon successful completion of SmartResource development activities, Metso Automation has decided to privately fund a separate project to research and develop a solution for integration of enterprise maintenance data in paper industry. SWIMMER The main project objectives are: to collect online maintenance data from several predefined data sources and store it in a semantic form; to build tools, which ease the enrichment of the data based on operation of several distributed applications; mining collected data and infer an implicit knowledge, which may be of interest to users.

20. Portal for SCOMA Web Community

21. UBIMATH: Network of Mathematical Models in Ubiquitous Environments

22. SMONT: Ontologies as Smart Resources

23. WIKI-Pro: Wikis as Smart Resources

24. MIT Data Center – IOG Cooperation We will cooperate on study "M-Language vs. OWL/RDF" from various angles:  (a) M-Language as complementary to OWL/RDF (where RDF considered as such and also as extended to RscDF, RgbDF, RpiDF);  (b) OWL/RDF as complementary to M-Language;  (c) semantic adapter M-Language - OWL/RDF;  (d) semantic adapter OWL/RDF - M-Language;  (e) M-Language + OWL/RDF = towards Global RFID;  (f) M-Language +OWL/RDF+ Mathematical Models + UBIWARE = Semantic Web of Things. David L. Brock Founder and Director, The Data Center Principal Research Scientist, MIT dlb@mit.edu Edmund W. Schuster Co-director, The Data Center Principal Research Scientist, MIT edmund_w@mit.edu

25. Berkeley- IOG Cooperation User modeling of activity integrating various sensing (e.g. microphones, GPS, occupancy sensors, etc.) and building applications on top. Professor John Canny, Berkeley Institute of Design, University Berkeley, California

26. Aachen - IOG Cooperation Security of mobile service provisioning Resource discovery in mobile P2P networks Professor Matthias Jarke, Information Systems Group, RWTH Aachen University, Germany

27. Cooperation with Technion, Israel Reliability and risk analysis, system survivability enhancement, reliability engineering, reliability optimization, optimal defense strategy against intentional attacks Professor Gregory Levitin, Faculty of Industrial Engineering & Management, Technion - Israel Institute of Technology Engineer-Expert. Reliability & Equipment Department. R&D Division. The Israel Electric Corporation Ltd

28. Cooperation with Free University of Amsterdam Dynamics of agent behavior Multiagent systems’ design Prof. Jan Treur Diagnostics of organizations Semantic Web Reasoning about diagnostic systems Ontology Learning Ontology Integration Prof. Frank Harmelen Dr. Borys Omelayenko

29. Cooperation with Kharkov National University of Radioelectronics Neuro-Fuzzy Networks Machine Learning Dr. Oleksandra Vitko Prof. Yevgeniy Bodyanskiy Industrial Automation Bayesian Networks Data Mining and Knowledge Discovery Volodymyr Kushnaryov Kharkov National University of Radioelectronics Ukraine Industrial diagnostics, prediction and control Telemedicine Ontologies in Education Statistics Prof. Natalya Lesna

30. Cooperation with ITIN, France Prof. Alain Gourdin Managing Director ITIN, IT-Institute Cergy-Pontoise cedex France www.itin.fr Integrating of Semantic Web and Agent Technologies with Telecommunications and Mobile Computing and its utilization in Business and Education