Clustering Services Based on Community Detection in Service Networks

Shiyuan Zhou and Yinglin Wang

Mathematical Problems in Engineering, 2019, vol. 2019, 1-11

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 (atomic service similarity) to characterize the atomic service similarity as a whole, which is a linear combination of (composite-sharing similarity) and (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 metric is promising in improving the performance of service clustering approaches.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:1495676

DOI: 10.1155/2019/1495676

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