Information Systems Frontiers

, Volume 16, Issue 1, pp 143–152 | Cite as

Multi-user web service selection based on multi-QoS prediction

  • Shangguang WangEmail author
  • Ching-Hsien Hsu
  • Zhongjun Liang
  • Qibo Sun
  • Fangchun Yang


In order to find best services to meet multi-user’s QoS requirements, some multi-user Web service selection schemes were proposed. However, the unavoidable challenges in these schemes are the efficiency and effect. Most existing schemes are proposed for the single request condition without considering the overload of Web services, which cannot be directly used in this problem. Furthermore, existing methods assumed the QoS information for users are all known and accurate, and in real case, there are always many missing QoS values in history records, which increase the difficulty of the selection. In this paper, we propose a new framework for multi-user Web service selection problem. This framework first predicts the missing multi-QoS values according to the historical QoS experience from users, and then selects the global optimal solution for multi-user by our fast match approach. Comprehensive empirical studies demonstrate the utility of the proposed method.


Web services Service selection QoS Multi-user QoS prediction 



The work presented is supported by the NSFC (61202435); NSFC (61272521); Natural Science Foundation of Beijing under Grant No.4132048; Specialized Research Fund for the Doctoral Program of Higher Education under Grant No. 20110005130001; Program for New Century Excellent Talents in University of China under Grant No.NCET-10-0263; Innovative Research Groups of the National Natural Science Foundation under Grant No.61121061; and 863 (2012AA111601).


  1. Zeng, L., Benatallah, B., Dumas, M., Kalagnanam, J., Sheng, Q. (2003). Quality-driven Web services composition. Proc. the 12th International Conference on the World Wide Web, pp.411–421.Google Scholar
  2. Zeng, L., Benatallah, B., Ngu, A.H.H., Dumas, M., Kalagnanam, J., Chang, H. (2004). QoS-aware middleware for Web services composition, vol. 30, no.5. IEEE Transaction on Software Engineering, IEEE Computer Society, pp. 311–327.Google Scholar
  3. Canfora, G., Penta, M.D., Esposito, R., Villani, M.L. (2005). An approach for QoS-aware service composition based on genetic algorithms. Proc. the 2005 conference on Genetic and Evolutionary Computation, pp.1069–1075.Google Scholar
  4. Alrifai, M., Risse, T. (2009). Combining global optimization with local selection for efficient QoS-aware service composition. Proc. the 18th International Conference on the World Wide Web, pp.881–890.Google Scholar
  5. Alrifai, M., Skoutas, D., Risse, T. (2010). Selecting skyline services for QoS-based Web service composition. Proc. the 19th International Conference on the World Wide Web, pp.11–20.Google Scholar
  6. Shahand, S., Turner, S. J., Cai, W., & Khademi, H. (2010). DynaSched: A dynamic Web service scheduling and deployment framework for data-intensive Grid workflows. Procedia Computer Science, 1(1), 593–602.CrossRefGoogle Scholar
  7. Dyachuk, D., Deters, R. (2006). Scheduling of composite web services. On the move to meaningful internet systems 2006: OTM 2006 Workshops, pp. 19–20.Google Scholar
  8. Kang, G., Liu, J., Tang, M., Liu, X., Fletcher, K.K. (2011) Web service selection for resolving conflicting service requests. Proc. IEEE International Conference on Web Service, pp. 387–394.Google Scholar
  9. Lo, W., Yin, J., Deng, S., Li, Y., Wu, Z. (2012) .An extended matrix factorization approach for QoS prediction in service selection. Proc. IEEE Ninth International Conference on Services Computing (SCC), pp. 162–169.Google Scholar
  10. Shao, L., Zhang, J., Wei, Y., Zhao, J., Xie, B., Mei, H. (2007) Personalized QoS prediction for Web services via collaborative filtering. proc. IEEE International Conference on Web Services, pp.439–446.Google Scholar
  11. Zheng, Z., Ma, H., Lyu, M.R., King, I. (2009). WSRec: A collaborative filtering based web service recommendation system. Proc. IEEE International Conference on Web Services, pp.437–444.Google Scholar
  12. Jiang, Y., Liu, J., Tang, M., Liu, X.F. (2011). An effective Web service recommendation method based on personalized collaborative filtering. Proc. IEEE International Conference on Web Services, pp.211–218.Google Scholar
  13. Zhang, L., Zhang, B., Liu, Y., Gao, Y., Zhu, Z. (2010). A Web service QoS prediction approach based on collaborative filtering. Proc. IEEE Asia-Pacific Services Computing Conference, pp.725–731.Google Scholar
  14. Chen, X., Liu, X., Huang, Z., Sun, H. (2010). RegionKNN: a scalable hybrid collaborative filtering algorithm for personalized Web service recommendation. Proc. IEEE International Conference on WebServices, pp.9–16.Google Scholar
  15. Tang, M., Jiang, Y., Liu, J., Liu, X. (2012). Location-aware collaborative filtering for QoS-based service recommendation. Proc. IEEE International Conference on Web Services, pp.202–209.Google Scholar
  16. Ortega, M., Rui, Y., Chakrabarti, K., Mehrotra, S., Huang, T.S. (1997) Supporting similarity queries in MARS. proc. the ACM Multimedia, pp.403–413.Google Scholar
  17. Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.CrossRefGoogle Scholar
  18. Yu, S. P., Yu, K., & Volker, T. (2006). Multi-output regularized feature projection. IEEE Transactions on Knowledge and Data Engineering, 18(12), 1600–1613.CrossRefGoogle Scholar
  19. Zheng, Z., Zhang, Y., Lyu, M. (2010). Distributed QoS evaluation for real-world Web services. Proc. IEEE International Conference on Web Services, pp.83–90.Google Scholar
  20. Breese, J., Heckerman, D., Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. Proc. the Fourteenth conference on Uncertainty in artificial intelligence, pp.43–52.Google Scholar
  21. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J., (1994) GroupLens: An open architecture for collaborative filtering of netnews. Proc. the 1994 ACM conference on Computer supported cooperative work, pp.175–186.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Shangguang Wang
    • 1
    Email author
  • Ching-Hsien Hsu
    • 2
  • Zhongjun Liang
    • 1
  • Qibo Sun
    • 1
  • Fangchun Yang
    • 1
  1. 1.State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijingChina
  2. 2.Department of Computer Science and Information EngineeringChung Hua UniversityHsinchuTaiwan

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