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

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Abstract

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.

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References

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

    Article  Google Scholar 

  • Dyachuk, D., Deters, R. (2006). Scheduling of composite web services. On the move to meaningful internet systems 2006: OTM 2006 Workshops, pp. 19–20.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • Ortega, M., Rui, Y., Chakrabarti, K., Mehrotra, S., Huang, T.S. (1997) Supporting similarity queries in MARS. proc. the ACM Multimedia, pp.403–413.

  • Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.

    Article  Google Scholar 

  • Yu, S. P., Yu, K., & Volker, T. (2006). Multi-output regularized feature projection. IEEE Transactions on Knowledge and Data Engineering, 18(12), 1600–1613.

    Article  Google Scholar 

  • 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.

  • 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.

  • 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.

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Acknowledgments

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).

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Correspondence to Shangguang Wang.

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Wang, S., Hsu, CH., Liang, Z. et al. Multi-user web service selection based on multi-QoS prediction. Inf Syst Front 16, 143–152 (2014). https://doi.org/10.1007/s10796-013-9455-4

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  • DOI: https://doi.org/10.1007/s10796-013-9455-4

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