Web service clustering using multidimensional angles as proximity measures

Abstract

Increasingly, application developers seek the ability to search for existing Web services within large Internet-based repositories. The goal is to retrieve services that match the user's requirements. With the growing number of services in the repositories and the challenges of quickly finding the right ones, the need for clustering related services becomes evident to enhance search engine results with a list of similar services for each hit. In this article, a statistical clustering approach is presented that enhances an existing distributed vector space search engine for Web services with the possibility of dynamically calculating clusters of similar services for each hit in the list found by the search engine. The focus is laid on a very efficient and scalable clustering implementation that can handle very large service repositories. The evaluation with a large service repository demonstrates the feasibility and performance of the approach.