Efficient query of quality correlation for service composition

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

41 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
Pages (from-to)695-709
Number of pages15
JournalIEEE Transactions on Services Computing
Volume14
Issue number3
Early online date27 Apr 2018
DOIs
Publication statusPublished - May 2021

Keywords

  • Quality Correlation
  • Service Composition
  • Quality of Service
  • Aggregation Algorithm
  • index graph
  • Quality correlation
  • aggregation algorithm
  • quality of service
  • service composition

Fingerprint

Dive into the research topics of 'Efficient query of quality correlation for service composition'. Together they form a unique fingerprint.

Cite this