Selecting skyline services for QoS-based web service composition
Proceedings of the 19th international conference on World wide web
A flexible graph-based approach for matching composite semantic web services
Proceedings of the 1st International Workshop on Linked Web Data Management
Web Service management system for bioinformatics research: a case study
Service Oriented Computing and Applications
Graph-based matching of composite OWL-S services
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications
A fuzzy framework for selecting top-k web service compositions
ACM SIGAPP Applied Computing Review
A QoS evaluation method for personalized service requests
WISM'11 Proceedings of the 2011 international conference on Web information systems and mining - Volume Part II
Multi-attribute optimization in service selection
World Wide Web
Scatter/Gather browsing of web service QoS data
Future Generation Computer Systems
Modelling and exploring historical records to facilitate service composition
International Journal of Web and Grid Services
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As the web is increasingly used not only to find answers to specific information needs but also to carry out various tasks, enhancing the capabilities of current web search engines with effective and efficient techniques for web service retrieval and selection becomes an important issue. Existing service matchmakers typically determine the relevance between a web service advertisement and a service request by computing an overall score that aggregates individual matching scores among the various parameters in their descriptions. Two main 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 Information Retrieval similarity measures up to semantic logic-based inference rules. Second, the reduction of individual scores to an overall similarity leads to significant information loss. Determining appropriate weights for these intermediate scores requires knowledge of user preferences, which is often not possible or easy to acquire. Instead, using a typical aggregation function, such as the average or the minimum of the degrees of match across the service parameters, introduces undesired bias, which often reduces the accuracy of the retrieval process. Consequently, several services, e.g., those having a single unmatched parameter, may be excluded from the result set, while being potentially good candidates. In this work, we present two complementary approaches that overcome the aforementioned deficiencies. First, we propose a methodology for ranking the relevant services for a given request, introducing objective measures based on dominance relationships defined among the services. Second, we investigate methods for clustering the relevant services in a way that reveals and reflects the different trade-offs between the matched parameters. We demonstrate the effectiveness and the efficiency of our proposed techniques and algorithms through extensive experimental evaluation on both real requests and relevance sets, as well as on synthetic scenarios.