Clustering Services Based on Community Detection in Service Networks. (2nd December 2019)

• Record Type: Journal Article
• Authors: Zhou, Shiyuan; Wang, Yinglin
• Other Names: Chai Chunlai Guest Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2019(2019)
• Publication Date: 2019-12-02
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2019/1495676
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : Service-oriented computing has become a promising way to develop software by composing existing services on the Internet. However, with the increasing number of services on the Internet, how to match requirements and services becomes a difficult problem. Service clustering has been regarded as one of the effective ways to improve service matching. Related work shows that structure-related similarity metrics perform better than semantic-related similarity metrics in clustering services. Therefore, it is of great importance to propose much more useful structure-related similarity metrics to improve the performance of service clustering approaches. However, in the existing work, this kind of work is very rare. In this paper, we propose a SCAS (service clustering approach using structural metrics) to group services into different clusters. SCAS proposes a novel metric A 2 S (atomic service similarity) to characterize the atomic service similarity as a whole, which is a linear combination of C 2 S (composite-sharing similarity) and A 3 S (atomic-service-sharing similarity). Then, SCAS applies a guided community detection algorithm to group atomic services into clusters. Experimental results on a real-world data set show that our SCAS performs better than the existing approaches. Our A 2 S metric is promising in improving the performance of service clustering approaches.