The idea of a decentralised, self-organising service-oriented architecture seems to be more and more plausible than the traditional registry-based ones in view of the success of the web and the reluctance in taking up web service technologies. Automatically clustering Web Service Description Language (WSDL) files on the web into functionally similar homogeneous service groups can be seen as a bootstrapping step for creating a service search engine and, at the same time, reducing the search space for service discovery. This paper proposes techniques to automatically gather, discover and integrate features related to a set of WSDL files and cluster them into naturally occurring service groups. Despite the lack of a common platform for assessing the performance of web service cluster discovery, our initial experiments using real-world WSDL files demonstrated the great potential of the proposed techniques.
Liu and Wong REF proposed techniques to gather, discover and integrate features related to a set of WSDL documents and cluster them into functionally similar homogeneous service communities.
Web service clustering using text mining techniques
IJAOSE
IJAOSE
Computer Science