PLMwsp

A probabilistic latent model for web service QoS prediction

Bobaker Mohamed A Madi, Quan Z. Sheng, Lina Yao, Yongrui Qin, Xianzhi Wang

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

5 Citations (Scopus)

Abstract

With the unprecedented and dramatic development of Web services in recent years, designing novel approaches for efficient Web service prediction has become of paramount importance. Quality of Service (QoS) plays a critical role in Web service recommendation. However determining QoS values of Web services is still a challenging task. For example, some QoS properties (e.g., response time, throughput) may hold different values for different users. In this paper, we describe how to develop a novel approach, PLMwsp, based on a probabilistic latent model, to predict effectively the QoS values of Web services. A Web service prediction has been developed, and experiments have been conducted to show the efficacy of our approach.

Original languageEnglish
Title of host publicationICWS 2016
Subtitle of host publicationIEEE International Conference on Web Services : proceedings
EditorsStephan Reiff-Marganiec
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages623-630
Number of pages8
ISBN (Electronic)9781509026753
DOIs
Publication statusPublished - 31 Aug 2016
Externally publishedYes
Event23rd IEEE International Conference on Web Services, ICWS 2016 - San Francisco, United States
Duration: 27 Jun 20162 Jul 2016

Other

Other23rd IEEE International Conference on Web Services, ICWS 2016
CountryUnited States
CitySan Francisco
Period27/06/162/07/16

Keywords

  • Probabilistic latent model
  • Quality of service
  • Web service prediction
  • Web services

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  • Cite this

    Madi, B. M. A., Sheng, Q. Z., Yao, L., Qin, Y., & Wang, X. (2016). PLMwsp: A probabilistic latent model for web service QoS prediction. In S. Reiff-Marganiec (Ed.), ICWS 2016: IEEE International Conference on Web Services : proceedings (pp. 623-630). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICWS.2016.86