A Context-aware Service Selection Approach based on Historical Records

Lianyong Qi*, Xuyun Zhang, Yiping Wen, Yuming Zhou

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

1 Citation (Scopus)


Due to the unstable network environment and fake quality propagation, the published service quality by service providers is not always trustable. Therefore, it becomes a necessity to evaluate the real quality of a web service, based on the service's historical records. However, for a web service, its multiple historical records often vary in execution context (e.g., execution time, user input, user location, etc.), which brings a great challenge to discriminate and rank all the historical records of an identical web service. Besides, for all the candidate web services, their historical record number (i.e., the times that a service was invoked) may be different, which may also affect the user's final service selection decision. In view of these challenges, we put forward a novel service selection approach CSS_HR (Context-aware Service Selection based on Historical Records). In CSS_HR, we first quantify the weight of each historical record, based on its context similarity with current user's service invocation; and afterwards, we quantify the weight of each candidate web service based on its historical record number; finally, with the derived weights of historical records and weights of candidate services, we evaluate all the candidate services and select a quality-optimal one. Through a set of experiments, we validate the feasibility of our proposal.

Original languageEnglish
Title of host publicationProceedings, 2015 International Conference on Cloud Computing and Big Data
Place of PublicationLos Alamitos, CA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Electronic)9781467383509
Publication statusPublished - 2015
Externally publishedYes
EventInternational Conference on Cloud Computing and Big Data - Shanghai
Duration: 4 Nov 20156 Nov 2015

Publication series

NameInternational Conference on Cloud Computing and Big Data-CCBD
ISSN (Print)2378-3680


ConferenceInternational Conference on Cloud Computing and Big Data


  • context
  • historical record
  • service selection
  • weight

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