Top-k dominant Web services under multi-criteria matching

Skoutas, D; Sacharidis, D; Simitsis, A; Kantere, V; Sellis, T

dc.contributor.author	Skoutas, D	en
dc.contributor.author	Sacharidis, D	en
dc.contributor.author	Simitsis, A	en
dc.contributor.author	Kantere, V	en
dc.contributor.author	Sellis, T	en
dc.date.accessioned	2014-03-01T02:46:33Z
dc.date.available	2014-03-01T02:46:33Z
dc.date.issued	2009	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32715
dc.subject	Efficient Algorithm	en
dc.subject	Experimental Evaluation	en
dc.subject	Inference Rule	en
dc.subject	Information Loss	en
dc.subject	Information Retrieval	en
dc.subject	Similarity Metric	en
dc.subject	Web of Data	en
dc.subject	Web Search Engine	en
dc.subject	Web Service	en
dc.subject.other	Efficient algorithm	en
dc.subject.other	Existing method	en
dc.subject.other	Experimental evaluation	en
dc.subject.other	Inference rules	en
dc.subject.other	Information loss	en
dc.subject.other	Matching criterion	en
dc.subject.other	Matching score	en
dc.subject.other	Multi-criteria	en
dc.subject.other	Objective measure	en
dc.subject.other	Service requests	en
dc.subject.other	Similarity metrics	en
dc.subject.other	Web search engines	en
dc.subject.other	Worst case scenario	en
dc.subject.other	Algorithms	en
dc.subject.other	Database systems	en
dc.subject.other	Information retrieval	en
dc.subject.other	Information services	en
dc.subject.other	Search engines	en
dc.subject.other	Web services	en
dc.subject.other	Parameter estimation	en
dc.title	Top-k dominant Web services under multi-criteria matching	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1145/1516360.1516463	en
heal.identifier.secondary	http://dx.doi.org/10.1145/1516360.1516463	en
heal.publicationDate	2009	en
heal.abstract	As we move from a Web of data to a Web of services, enhancing the capabilities of the current Web search engines with effective and efficient techniques for Web services retrieval and selection becomes an important issue. Traditionally, the relevance of a Web service advertisement to a service request is determined by computing an overall score that aggregates individual matching scores among the various parameters in their descriptions. Two drawbacks characterize such approaches. First, there is no single matching criterion that is optimal for determining the similarity between parameters. Instead, there are numerous approaches ranging from using Information Retrieval similarity metrics up to semantic logic-based inference rules. Second, the reduction of individual scores to an overall similarity leads to significant information loss. Since there is no consensus on how to weight these scores, existing methods are typically pessimistic, adopting a worst-case scenario. As a consequence, several services, e.g., those having a single unrelated parameter, can be excluded from the result set, even though they are potentially good alternatives. In this work, we present a methodology that overcomes both deficiencies. Given a request, we introduce an objective measure that assigns a dominance score to each advertised Web service. This score takes into consideration all the available criteria for each parameter in the request. We investigate three distinct definitions of dominance score, and we devise efficient algorithms that retrieve the top-k most dominant Web services in each case. Extensive experimental evaluation on real requests and relevance sets, as well as on synthetically generated scenarios, demonstrates both the effectiveness of the proposed technique and the efficiency of the algorithms. Copyright 2009 ACM.	en
heal.journalName	Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, EDBT'09	en
dc.identifier.doi	10.1145/1516360.1516463	en
dc.identifier.spage	898	en
dc.identifier.epage	909	en