Efficient Query of Quality Correlation for Service Composition

Yiwen Zhang, Guangming Cui, Shuiguang Deng, Feifei Chen, Yan Wang, Qiang He

Research output: Contribution to journalArticle

46 Citations (Scopus)

Abstract

As enterprises around the globe embrace globalization, strategic alliances among enterprises have become an important means to gain competitive advantages. Enterprises cooperate to improve the quality or lower the prices of their services, which introduce quality correlations, i.e., the quality of a service is associated with other services. Existing approaches for service composition have not fully and systematically considered the quality correlations between services. In this paper, we propose a novel approach named Q2C (Query of Quality Correlation) to systematically model quality correlations and enable efficient queries of quality correlations for service compositions. Given a service composition and a set of candidate services, Q2C first preprocesses the quality correlations among the candidate services and then constructs a quality correlation index graph to enable efficient queries for quality correlations. Extensive experiments are conducted on a real-world web service dataset to demonstrate the effectiveness and efficiency of Q2C.
Original languageEnglish
JournalIEEE Transactions on Services Computing
DOIs
Publication statusE-pub ahead of print - 27 Apr 2018

Keywords

  • Aggregation Algorithm
  • Correlation
  • Electronic mail
  • Heuristic Integer Programming
  • Index Graph
  • Indexes
  • Quality Correlation
  • Quality of Service
  • Service Composition
  • Service-oriented architecture
  • Task analysis

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