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dc.date.accessioned2007-11-19T13:09:25Z
dc.date.available2007-11-19T13:09:25Z
dc.date.issued2007-11-19T13:09:25Z
dc.identifier.uriurn:nbn:de:hebis:34-2007111919638
dc.identifier.urihttp://hdl.handle.net/123456789/2007111919638
dc.format.extent443589 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.subjectWeb Service Compositioneng
dc.subjectWeb Service Matchingeng
dc.subjectSemanticeng
dc.subjectSemantic Matchingeng
dc.subjectUninformed Searcheng
dc.subjectIterateng
dc.subjectWeb Service Challengeeng
dc.subjectWSCeng
dc.subjectSemantic Compositionseng
dc.subjectSemantic Webeng
dc.subjectIDDFSeng
dc.subjectIterative Deepening Depth-First Searcheng
dc.subjectGreedy Searcheng
dc.subjectGenetic Algorithmeng
dc.subjectBest-First Searcheng
dc.subject.ddc004
dc.titleWeb Service Composition Systems for the Web Service Challenge - A Detailed Revieweng
dc.typeTechnischer Report
dcterms.abstractThis report gives a detailed discussion on the system, algorithms, and techniques that we have applied in order to solve the Web Service Challenges (WSC) of the years 2006 and 2007. These international contests are focused on semantic web service composition. In each challenge of the contests, a repository of web services is given. The input and output parameters of the services in the repository are annotated with semantic concepts. A query to a semantic composition engine contains a set of available input concepts and a set of wanted output concepts. In order to employ an offered service for a requested role, the concepts of the input parameters of the offered operations must be more general than requested (contravariance). In contrast, the concepts of the output parameters of the offered service must be more specific than requested (covariance). The engine should respond to a query by providing a valid composition as fast as possible. We discuss three different methods for web service composition: an uninformed search in form of an IDDFS algorithm, a greedy informed search based on heuristic functions, and a multi-objective genetic algorithm.eng
dcterms.accessRightsopen access
dcterms.creatorWeise, Thomas
dcterms.creatorBleul, Steffen
dcterms.creatorGeihs, Kurt
dcterms.isPartOfKasseler Informatikschriftenger
dcterms.isPartOf2007, 7ger
dc.description.everythingData-Mining-Cup 2007eng
dc.subject.ccsG.3
dc.subject.ccsI.2.4
dc.subject.ccsI.2.6
dc.subject.ccsI.5.0
dc.subject.ccsC.2.2
dc.subject.ccsC.2.4
dc.subject.ccsJ.1


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