SmartResource: Proactive Self-Maintained Resources in Semantic Web

1. SmartResource:Proactive Self-Maintained Resources in Semantic Web TEKES Project proposal Vagan Terziyan, Project Leader Industrial Ontologies Group Agora Center, University of Jyväskylä

2. Our Team and Consortium University of Jyväskylä Industrial Ontologies Group (SmartResource) “Industrial Ontologies” Group:http://www.cs.jyu.fi/ai/OntoGroup/index.html

3. Networked Business Environments Some purposes of NBE development : • knowledge management: • mining, accumulation and sharing of expert knowledge within the whole enterprise • knowledge business: • deliver gained enterprise experience to external market • new (value-added) services and solutions • integrated business solutions “In a networked business environment Metso will be a business hub controlling the flow of information in the network of installed Metso devices and solutions, and Metso’s customers and partners.” (Future Care) Semantic Web technology provides standards for metadata and ontology development such as semantic annotations (Resource Description Framework) and knowledge representation (Web Ontology Language). It facilitates interoperability of heterogeneous components, authoring reusable data and intelligent, automated processing of data. Semantic Web is an enabling technology for the future Networked Business Environment

4. Emerging Semantic Web application areas: • Knowledge Management • e-Business • Enterprise Application Integration in Bringing New Value to the Data • Reusing data • Sharing data • Integrating data Networked Business Environment requires new advanced ways of data and knowledge management Industrial Maintenance domain is a good application case for the concept of the Networked Business Environment Networked Maintenance Environmentwill bring all benefits of the knowledge management, delivering value-added services and integration of businesses

5. S m a r t R e s o u r c e PROJECT WIDER OBJECTIVE - to combine the emergingSemantic Web, Web Services, Peer-to-Peer, Machine LearningandAgenttechnologies for the development of a global and smart maintenance management environment, to provide Web-based support for the predictive maintenance of industrial devices by utilizing heterogeneous and interoperable Web resources, services and human experts Tekes Project Application, Submitted January 2004

6. Industrial Resources Classes of resources in maintenance systems: • Devices - increasingly complex machines, equipment, etc., that require costs-demanding support • Processing Units (Services) – embedded, local and remote systems, for automated intelligent monitoring, diagnostics and control over devices • Humans (Experts) – qualified users of the system, operators, maintenance experts, a limited resource that should be reused

7. GUN MAIN RESEARCH OBJECTIVE Our intention is to provide tools and solutions to make heterogeneous industrial resources (files, documents, services, devices, processes, systems, human experts, etc.) web-accessible, proactive and cooperative in a sense that they will be able to analyze their state independently from other systems or to order such analysis from remote experts or Web-services to be aware of own condition and to plan behavior towards effective and predictive maintenance. Global Understanding eNvironment

8. On-line learning “Services” Maintenance data exchange Smart Maintenance Environment “Experts” “Devices with on-line data” exchange data Maintenance

9. “Expert” “Device” RSCDF “Service” Project Objectives (Year 1) Define Semantic Web-based framework for unification of maintenance data and interoperability in maintenance system Research and Development: • Resource State/Condition Description Framework (RSCDF) based on Semantic Web and extension of RDF (Resource Description Framework) • RSCDF adapters (wrappers) for devices, services and experts: - browsable devices - application-expert interface -RSCDF-enabled services

10. “Device” Resource Agent ”Adapter” Smart Maintenance Environment Remotediagnostics “Service” “Device” “Expert” “Expert” “Service” Expert~Service Service learning and remotediagnostics Project Objectives (Year 2) Development of agent-based resource management framework and enabling meaningful resource interaction • Adding agents to resources • Enabling resource proactive behavior. Designing Resource Goal/Behavior Description Framework (RGBDF - Lite) • Designing agents to maintain resources (RGBDF Engine) • Implementation of agent-communication scenarios • service learning • remote diagnostics Lite

11. Project Objectives (Year 3) • Development of P2P agent-communication system • Resource Discovery • Maintenance Data & Knowledge Integration • Certification and credibility assessment of services • Research of the Resource Goal/Behavior Description Framework • Semantic modelling of a resource proactive behaviour • Exchanging & integrating models of resource (maintenance) behaviour • Testing “on-the-field” using • Real devices • Existing diagnostic software as Web-services • Experts Development of networked maintenance environment

12. “Expert” Network “Expert” “Device” Network “Device” Labelled data User interface Resource Agent History data ”Adapter” RSCDF data Resource Agent results diagnostic “RSCD Alarm Service” “Embedded Alarm Service” Remote Expert Platform RSCDF data ”Adapter” RSCDF data ”Adapter” Sensor data Sensor data Querying data Labelled Labelled data Labelled data Learning “Service” Local (Embedded) Platform Resource Agent sample and querying diagnostic results Diagnostic model “Service” Network ”Adapter” RSCDF data Learningprocess Remote Service Platform Maintenance Networking Environment Semantic Web environment

13. P2P networking - network of hubs - highly scalable - fault-tolerable • supports dynamic changes • of network structure • does not need • administration Why to interact? • Resource summarizes “opinions” from multiple services; • Services “learns” from multiple teachers; • One service for multiple similar clients; • Resources exchange lists of services; • Services exchange lists of clients.

14. Device will support service composition in form of ensembles using own models of service quality estimation. Service composition is made with goal of increasing diagnostic performance. Learning sample Labelled data Test sample Learning and test sample. Querying diagnostic results. Labelled data … Diagnostic model Diagnostic model Integrating services Evaluation and Result integration mechanism w1 w5 w2 w4 w3 “Device” Labelled data “Service” “Service”

15. … Device Class-specific diagnostic model Device-specific diagnostic model … Integrating knowledge “Service” Service builds classification model; many techniques are possible, e.g.: • own model for each device; • one model from several devices of the same type (provides device experience exchange) . Diagnostic model 1 Diagnostic model n “Device” “Device” “Device” Labelled data “Device” “Device” Labelled data “Device” Labelled data Labelled data Labelled data Labelled data

16. Service 1 Service 2 Service 3 5 3 4 Device 6 1 trust 2 Own evaluations Certifying party Certification Sure, there are security threats as in any open environment. Security is to be ensured using existing solutions for Internet environment. Existence of certification authorities is required in the network. Certificates gained by services and trust to the certificate issuer are factors that influence optimal service selection. The quality of service is evaluated by users as well.

17. Development Stages Year 1: Resource Adapters to the RSCDF-based unification of resource data; Remote resource access in Semantic Web environment Year 2: Resource Agents for remote diagnostics; Learnability of services Year 3: Support for semantic P2Pnetworking and diagnostic services integration Project will produce 3 versions of prototype software by implementing the following components and functionality:

18. P2P environment that integrates many devices, many services, many human experts and supports: Interaction ”One service – many devices” Unification of maintenance data Resource Agent Service Project Results Adaptation of resources (devices, services, experts) to the Environment Support for services that are able to learrn Discovery of necessary network components using their profiles Research Results: RSCDF RGBDF Proactive Resources P2P Maintenance Interaction”One device – many services”

19. Funding Plans New partners …are warmly welcome!

20. Obtain More Information about SmartResource from: Head of SmartResource Industrial Consortium (Steering Committee Head) Dr. Jouni Pyötsiä, Metso Automation Oy. Jouni.Pyotsia@metso.com , Tel.: 040-548-3544 SmartResource Contact Person Prof. Timo Tiihonen, Vice-Rector, University of Jyväskylä tiihonen@it.jyu.fi , Tel.: 014-260-2741 SmartResource Project Leader Prof. Vagan Terziyan, Agora Center, University of Jyväskylä vagan@it.jyu.fi , Tel.: 014-260-4618

21. Obtain More Information about SmartResource from: Presentation of our group: http://www.cs.jyu.fi/ai/OntoGroup/IOG_Presentation.ppt Sample of presentation of our SmartResource project activities: http://www.cs.jyu.fi/ai/Madeira.ppt  (in text: http://www.cs.jyu.fi/ai/Smart_Resource.doc ) Some relevant research papers of our group: http://www.cs.jyu.fi/ai/Mobile_Components.doc http://research.i2r.a-star.edu.sg/iaamsad/ijcss/Journals/Vol4No2/2003-2-terzijan-5.PDF http://www.cs.jyu.fi/ai/papers/IJWSR-2004.pdf More papers of our group: http://www.cs.jyu.fi/ai/vagan/papers.html Web sites of our group with more information: http://www.cs.jyu.fi/ai http://www.cs.jyu.fi/ai/OntoGroup