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Semantic Web Services Composition via Planning as Model Checking

Semantic Web Services Composition via Planning as Model Checking. Hong Qing Yu and Dr. Stephan Reiff-Marganiec Computer Science Department. Introduction. Background and the framework of SWSC Case study Web Services model Planning as model checking Advantages and future work.

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Semantic Web Services Composition via Planning as Model Checking

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  1. Semantic Web Services Composition via Planning as Model Checking Hong Qing Yu and Dr. Stephan Reiff-Marganiec Computer Science Department

  2. Introduction • Background and the framework of SWSC • Case study • Web Services model • Planning as model checking • Advantages and future work

  3. Background of SWS Syntax only! WS standards: Lack of semantics! Web Service Architecture

  4. Semantic Web Services • What should S+WS ontologies provide? (Mainly) Automation of the Usage Process: • Publication • Discovery • Selection • Composition • Execution • Monitoring

  5. The framework of SWSC Phase 1 : Specification Specify the Planning Goal Provide the initial situation WS Repository Phase 2 : Model extraction Select WS which in the plan domain Extract WS models Ontologies Phase 3 : Planning Phase 4 : Physical Composition & Execution Selection Generation Execution

  6. Case study of Web Services Composition Services WS1=Locate IP Initial Situation Smart Portal WS2= TV Information WS3= TV shop (S) && After WS4= Item delivery (D) I) (D S WS5= Insurance (I) Goal WS6= TV License

  7. WS model (Type, Role) Precondition Input message (Parameters) Operation Name Domain Communications Purpose Quality Output message (Parameters) State Operation Operation Operation Operation

  8. WS model Got_TVL Min (string Brand, double S_size, string Type, string Location, string TV_license ) Min (string IP_address) WS1 WS2 WS3 Confirm E-shopping E-shopping E-shopping Select Locating TV_infor TV_sell Request high high high Mout (string Location) Mout (string Brand, string Type, double S_size, string review, Colo_type) Mout (string S_adress, double value, double TV_size) Located Got_infor Purchased Purchased Located Min (double value, string C_address, string Goods_type) Min (string S_address string Location, double size) Min (string C_address, string Colo_type) WS4 WS5 WS6 Confirm Confirm Select E-shopping E-shopping E-shopping Delivery_item Insurance TV_license Request Request Request high high high Mout (date delivery_time, double cost) Mout (string reference) Mout (string TV_license) Deliveried_Item Bought_insurance Got_TVL

  9. Composition problem model • Specification for the goal • Specification for start conditions and data • We are planning from initial operation state • The initial knowledge is the information which submitted by Client user • Our case • Initial state is start • Initial knowledge is Customer address, Goods type (TV), IP address I) (D S

  10. Planning as Model Checking State: {Start, Located, Got_infor, s Got_TVL, Purchased, Delivered_item, 1 2 Bought_insurance} 2 Parameter: {string C_address, string Goods_type, string IP_address, 1 string Location, string TV_license 6 old string Brand, string Type, double S_size, string review, Colo_type, old string S_adress, double value, double TV_size, 3 date delivery_time, double cost, 4 old 5 string reference} 5 4 I) (D S

  11. Advantages and future work Advantages: • Not rely on any particular ontology language • Simple specification • Executable + Reusable Future Work: • More complex goals • Add non-functional requirement to planning algorithm • Interleaving of services in plans • Complete the framework

  12. Thanks Any Questions? TR available : http://www.cs.le.ac.uk/people/hqy1/swsc_pamc1.0.pdf

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