Effective web-service discovery using K-means clustering

Abstract

Web Services are proving to be a convenient way to integrate distributed software applications. As service-oriented architecture is getting popular, vast numbers of web services have been developed all over the world. But it is a challenging task to find the relevant or similar web services using web services registry such as UDDI. Current UDDI search uses keywords from web service and company information in its registry to retrieve web services. This information cannot fully capture user's needs and may miss out on potential matches. Underlying functionality and semantics of web services need to be considered. In this study, we explore the resemblance among web services using WSDL document features such as WSDL Content and Web Services name. We compute the similarity of web services and use this data to generate clusters using K-means clustering algorithm. This approach has really yielded good results and can be efficiently used by any web service search engine to retrieve similar or related web services. © 2013 Springer-Verlag Berlin Heidelberg.