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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

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Pervasive computing environments

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ChallengePervasive 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

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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

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Talk Outline

BackgroundBackground Semantic WS & WS conversationsSemantic WS & WS conversations

Ad hoc composition of User Tasks Semantic operation matching Conversation matching

Modeling conversations as finite state automata

Conclusion & Future work

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Semantic Web servicesWeb 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)

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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

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Talk outline

Background Semantic WS & WS conversations

Ad hoc Composition of User TasksAd hoc Composition of User Tasks Semantic operation matchingSemantic operation matching Conversation matchingConversation matching

Modeling conversations as finite state Modeling conversations as finite state automataautomata

Conclusion & Future work

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Conversation integration

Each environment’s service is described as a semantic Web service with a conversationThe user task is described as an abstract conversation

Abstract user task conversation

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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

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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

OntologiesPaolucci algorithm

CarSelling

CarVending

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Modeling conversations as finite state automataA model to map OWL-S conversations to finite state automataA 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

Sop3

op4

BA

choicesequence

B op2

sequence

OWL-S Conversation

ε

εop3

op1

S

op2

op4

A

B

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Conversation matching

Abstract task automaton

BrowseVideoDB

SearchVideo

Download

DisplayFilm

AdaptLight

DisplayFilm

DisplayGame

Sinit

Light Control Service (LCS)

PlasmaService (PS)

SearchVideo

TVMode

BrowseVideoDBVideoDBService (VS)

Automaton of the selected services

AdaptLight

CompositeControlService (CCS)

AdaptSound

AdaptLight

Download

Concrete task automaton

VS.BrowseVideoDB

VS.SearchVideo

VS.Download

PS.DisplayFilmLCS.AdaptLight

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Talk outlineBackground 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

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Conclusion & Future workAd 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])