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Knowledge and Information Systems

, Volume 36, Issue 3, pp 579–605 | Cite as

Efficient planning for top-K Web service composition

  • Shuiguang Deng
  • Bin Wu
  • Jianwei YinEmail author
  • Zhaohui Wu
Regular Paper

Abstract

This paper proposes a novel approach based on the planning-graph to solve the top-k QoS-aware automatic composition problem of semantic Web services. The approach includes three sequential stages: a forward search stage to generate a planning-graph to reduce the search space of the following two stages greatly, an optimal local QoS calculating stage to compute all the optimal local QoS values of services in the planning, and a backward search stage to find the top-K composed services with optimal QoS values according to the planning-graph and the optimal QoS value. In order to validate the approach, experiments are carried out based on the test sets offered by the WS-Challenge competition 2009. The results show that the approach can find the same optimal solution as the champion system from the competition but also can provide more alternative solutions with the optimal QoS for users.

Keywords

Web service composition AI planning Planning-graph Top-K QoS-aware 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China under Grant No.61170033 and the National High-Tech Research and Development Plan of China under Grant 2011BAD21B02.

References

  1. 1.
    Wu ZH, Deng SG, Li Y et al (2009) Computing compatibility in dynamic service composition. Knowl Informn Syst 19(1):107–129MathSciNetCrossRefGoogle Scholar
  2. 2.
    Wu J, Chen L, Zenf ZB et al (2012) Clustering Web services to facilitate service discovery. Knowl Inform Syst (to appear)Google Scholar
  3. 3.
    María PC, Rafael B, Ismael S et al (2012) A semantic approach for the requirement-driven discovery of Web resources in the life sciences. Knowl Inform Syst. doi: 10.1007/s10115-012-0498-5
  4. 4.
    Papazoglou M, Traverso P, Dustdar S et al (2007) Service-oriented computing: state of the art and research challenges. IEEE Comput 40(11):38–45CrossRefGoogle Scholar
  5. 5.
    Peer J (2005) Web service composition as AI planning—a survey. http://logic.stanford.edu/serviceplanning/readinglist/pfwsc.pdf
  6. 6.
    McIlraith S, Son TC (2002) Adapting Golog for composition of semantic Web services. International conference on knowledge representation and reasoning, pp 482–493Google Scholar
  7. 7.
    Phan M, Hattori F (2006) Automatic Web service composition using ConGolog. IEEE international conference on distributed computing systems workshops, pp 17–22Google Scholar
  8. 8.
    Oh SC, Lee JY, Cheong SH et al (2009) WSPR*: Web-service planner augmented with A* algorithm. International conference on commerce and enterprise, computing, pp 515–518Google Scholar
  9. 9.
    Oh SC, On BW, Larson EJ et al (2005) BF*: Web Services discovery and composition as graph search problem. IEEE international conference on e-technology, e-commerce and e-service, pp 784–786Google Scholar
  10. 10.
    Wu B, Deng SG, Li Y et al (2011) AWSP: an automatic Web service planner based on heuristic state space search. International conference on Web services, pp 403–410Google Scholar
  11. 11.
    Naseri M, Tomhidi A (2007) QoS-aware automatic composition of Web services using AI planners. International conference on internet and Web applications and service, pp 29–35Google Scholar
  12. 12.
    Sirina E, Parsiab B, Wu D et al (2004) HTN planning for web service composition using SHOP2. Web Semantics: Sci Serv Agents World Wide Web 1(4):377–396CrossRefGoogle Scholar
  13. 13.
    Paik I, Maruyama D (2007) Automatic web services composition using combining HTN and CSP. International conference on computer and information technology, pp 206–211Google Scholar
  14. 14.
    Chen K, Xu JY, Reiff-Marganiec S (2009) Markov-HTN planning approach to enhance flexibility of automatic web services composition. International conference on Web services, pp 9–16Google Scholar
  15. 15.
    Li Y, Chen JL (2006) Automatic composition of semantic Web services—a theorem proof approach. Asian conference on the, semantic Web, pp 481–487Google Scholar
  16. 16.
    Rao JH, Kungas P, Matskin M (2003) Application of linear logic to Web service composition. International conference on Web services, pp 3–9Google Scholar
  17. 17.
    Rao JH, Kungas P, Matskin M (2004) Logic-based web services composition: from service description to process model. International conference on Web services, pp 446–453Google Scholar
  18. 18.
    Zheng XR, Yan YH (2008) An efficient syntactic Web service composition algorithm based on the planning-graph model. International conference on Web services, pp 691–699Google Scholar
  19. 19.
    Oh SC, Lee D, Kumara SRT (2007) Web service planner (WSPR): an effective and scalable web service composition algorithm. Int J Web Serv Res 4(1):1–23CrossRefGoogle Scholar
  20. 20.
    Li WQ, Dai XM, Jiang H (2010) Web services composition based on weighted planning-graph. International conference on networking and, distributed computing, pp 89–93Google Scholar
  21. 21.
    Li XG, Zhao QF, Dai Y (2010) A semantic Web service composition method based on an enhanced planning-graph. International conference on e-business and e-government, pp 2288–2291Google Scholar
  22. 22.
    Huang ZQ, Jiang W, Hu SL et al (2009) Effective pruning algorithm for QoS-aware service composition. IEEE conference on commerce and enterprise, computing, pp 519–522Google Scholar
  23. 23.
    Jiang W, Zhang C, Huang ZQ et al (2010) QSynth: a tool for QoS-aware automatic service composition. International conference of Web services, pp 42–49Google Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  • Shuiguang Deng
    • 1
  • Bin Wu
    • 1
  • Jianwei Yin
    • 1
    Email author
  • Zhaohui Wu
    • 1
  1. 1.College of Computer Science and TechnologyZhejiang UniversityHang ZhouChina

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