1. 1 Ad Hoc Composition of User Tasks in Pervasive Computing Environments Sonia Ben Mokhtar, Nikolaos Georgantas, Valérie Issarny ARLES Project, INRIA, France Software Composition (SC 2005) 9 April 2005, Edinburgh, Scotland

2. 2 Pervasive computing environments

3. 3 Challenge Pervasive computing environments are populated with : Mobile nodes that offer a number of heterogeneous services Mobile users that need to perform tasks using the available services at a specific time and place Challenge Allow users to perform tasks, by integrating on the fly available environment’s services Ad Hoc Composition of User Tasks Existing approaches commonly assume that services have been pre-developed to integrate, and perform syntactic matches between them

4. 4 Issues & Requirements Deal with a number of Issues Heterogeneity at the middleware layer (e.g. discovery and communication protocols) Heterogeneity at the application layer (e.g. service descriptions) Dynamic service invocation Dynamic service composition Build upon a number of paradigms SOA (Web services) : enable middleware interoperability Semantic Web : make service descriptions machine understandable Conversations : enable dynamic service invocation

5. 5 Talk Outline Background Semantic WS & WS conversations Semantic WS & WS conversations Ad hoc composition of User Tasks Semantic operation matching Conversation matching  Modeling conversations as finite state automata Conclusion & Future work

6. 6 Semantic Web services Web Services A software component developed using any language, deployed on any platform, described using WSDL, accessible via remote calls using SOAP on top of internet protocols Web Services in pervasive computing environments (e.g. WSAmI [ISTS04]) Semantic Web Services Make Web services’ descriptions machine understandable Use the solutions proposed by the semantic Web community for semantically annotating Web pages to Web services (ontologies)  Semantic annotation on top of WSDL (e.g. Meteor-S [WWW’04], WSDF [ICWS’04] )  Use Web services ontologies (e.g. OWL-S)

7. 7 Service description Interface level: signatures of the service’s operations Process level (conversation): external behaviour of a service Binding level: low-level information to interact with the service WSDL Interface level + Binding level  not sufficient for dynamic service invocation OWL-S Interface + Process + Binding  a complete solution enhancing WSDL a c d b a(in,out) b(in,out) c(in,out) d(in,out) WSDL description Conversation description Web service conversations

8. 8 Talk outline Background Semantic WS & WS conversations Ad hoc Composition of User Tasks Semantic operation matching Semantic operation matching Conversation matching Conversation matching  Modeling conversations as finite state automata Conclusion & Future work

9. 9 Conversation integration Each environment’s service is described as a semantic Web service with a conversation The user task is described as an abstract conversation Abstract user task conversation

10. 10 Ad hoc composition of user tasks Objective A semantic matching algorithm that reconstructs the conversation of an abstract user task from the conversations of the available services in the environment Semantic matching of conversations Match semantically the operations involved in the conversation Match the control constructs involved in the conversation

11. 11 Semantic operation matching Match the operations required by the abstract task with those offered by the environment services Select a set of services that provide semantically equivalent operations with those of the target abstract task Based on the algorithm by Paolucci et al. in [ISWC’02] for matching semantic Web services capabilities Semantic Reasoning Ontologies Paolucci algorithm CarSelling CarVending

12. 12 Modeling conversations as finite state automata A model to map OWL-S conversations to finite state automata A mapping rule for each OWL-S control construct (e.g. Choice, Sequence, While, Split) Transform the problem of dynamic conversation integration to an automata analysis problem Automaton representing the conversation op1 A S op3op4 B A choice sequence B op2 sequence OWL-S Conversation ε ε op3 op1 S op2 op4 A B

13. 13 Conversation matching Abstract task automaton BrowseVideoDB SearchVideo Download DisplayFilm AdaptLight DisplayFilm DisplayGame S init Light Control Service (LCS) Plasma Service (PS) SearchVideo TVMode BrowseVideoDB VideoDB Service (VS) Automaton of the selected services AdaptLight Composite Control Service (CCS) AdaptSound AdaptLight Download Concrete task automaton VS.BrowseVideo DB VS.SearchVideo VS.Download PS.DisplayFilm LCS.AdaptLight

14. 14 Talk outline Background Semantic WS & WS conversations Ad hoc composition of semantic Web services Semantic operation matching Conversation matching  Modeling conversations as finite state automata Conclusion & Future work

15. 15 Conclusion & Future work Ad hoc Composition of User Tasks in Pervasive Computing Environments A flexible approach for composing heterogeneous services to perform a user task Built upon Web services and Semantic Web paradigms Based on conversations integration Future work Prototype implementation underway as a part of the IST AMIGO project Integrate the operation matching step in a scalable service discovery protocol (e.g., Sailhan et. al. [PERCOM'05]) Consider QoS requirements of the user tasks (e.g., Liu et. al. [MDM'04])