Enhancing web service discovery using meta-heuristic CSO and PCA based clustering

Abstract

Web service discovery is one of the crucial tasks in service-oriented applications and workflows. For a targeted objective to be achieved, it is still challenging to identify all appropriate services from a repository containing diverse service collections. To identify the most suitable services, it is necessary to capture service-specific terms that comply with its natural language documentation. Clustering available Web services as per their domain, based on functional similarities would enhance a service search engine’s ability to recommend relevant services. In this paper, we propose a novel approach for automatically categorizing the Web services available in a repository into functionally similar groups. Our proposed approach is based on the Meta-heuristic Cat Swarm Optimization (CSO) Algorithm, further optimized by Principle Component Analysis (PCA) dimension reduction technique. Results obtained by experiments show that the proposed approach was useful and enhanced the service discovery process, when compared to traditional approaches. © Springer Nature Singapore Pte Ltd. 2018.