Skip to main content
SpringerLink
Log in

GP-Based Approach to Comprehensive Quality-Aware Automated Semantic Web Service Composition

  • Conference paper
  • First Online:
Simulated Evolution and Learning (SEAL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10593))

Included in the following conference series:

Abstract

Comprehensive quality-aware semantic web service composition aims to optimise semantic matchmaking quality and Quality of service (QoS) simultaneously. It is an NP-hard problem due to its huge search space. Therefore, heuristics have to be employed to generate near-optimal solutions. Existing works employ Evolutionary Computation (EC) techniques to solve combinatorial optimisation problems in web service composition. In particular, Genetic Programming (GP) has shown its promise. The tree-based representation utilised in GP is flexible to represent different composition constructs as inner nodes, but the semantic matchmaking information can not be directly obtained from the representation. To overcome this disadvantage, we propose a tree-like representation to directly cope with semantic matchmaking information. Meanwhile, a GP-based approach to comprehensive quality-aware semantic web service composition is proposed with explicit support for our representation. We also design specific genetic operation that effectively maintain the correctness of solutions during the evolutionary process. We conduct experiments to explore the effectiveness and efficiency of our GP-based approach using a benchmark dataset with real-world composition tasks.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Blum, A.L., Furst, M.L.: Fast planning through planning graph analysis. Artif. Intell. 90(1), 281–300 (1997)

    Article  MATH  Google Scholar 

  2. Feng, Y., Ngan, L.D., Kanagasabai, R.: Dynamic service composition with service-dependent QoS attributes. In: 2013 IEEE 20th International Conference on Web Services (ICWS), pp. 10–17. IEEE (2013)

    Google Scholar 

  3. Gupta, I.K., Kumar, J., Rai, P.: Optimization to quality-of-service-driven web service composition using modified genetic algorithm. In: 2015 International Conference on Computer, Communication and Control (IC4), pp. 1–6. IEEE (2015)

    Google Scholar 

  4. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection, vol. 1. MIT press, Cambridge (1992)

    MATH  Google Scholar 

  5. Küster, U., König-Ries, B., Krug, A.: Opossum-an online portal to collect and share SWS descriptions. In: 2008 IEEE International Conference on Semantic Computing, pp. 480–481. IEEE (2008)

    Google Scholar 

  6. Lécué, F.: Optimizing QoS-aware semantic web service composition. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 375–391. Springer, Heidelberg (2009). doi:10.1007/978-3-642-04930-9_24

    Chapter  Google Scholar 

  7. Lécué, F., Delteil, A., Léger, A.: Optimizing causal link based web service composition. In: ECAI. pp. 45–49 (2008)

    Google Scholar 

  8. Ma, H., Schewe, K.D., Thalheim, B., Wang, Q.: A formal model for the interoperability of service clouds. SOCA 6(3), 189–205 (2012)

    Article  Google Scholar 

  9. Ma, H., Wang, A., Zhang, M.: A hybrid approach using genetic programming and greedy search for QoS-aware web service composition. In: Hameurlain, A., Küng, J., Wagner, R., Decker, H., Lhotska, L., Link, S. (eds.) Transactions on Large-Scale Data- and Knowledge-Centered Systems XVIII. LNCS, vol. 8980, pp. 180–205. Springer, Heidelberg (2015). doi:10.1007/978-3-662-46485-4_7

    Google Scholar 

  10. Paolucci, M., Kawamura, T., Payne, T.R., Sycara, K.: Semantic matching of web services capabilities. In: Horrocks, I., Hendler, J. (eds.) ISWC 2002. LNCS, vol. 2342, pp. 333–347. Springer, Heidelberg (2002). doi:10.1007/3-540-48005-6_26

    Chapter  Google Scholar 

  11. Peer, J.: Web Service Composition as AI planning: A Survey. University of St. Gallen, Switzerland (2005)

    Google Scholar 

  12. Qi, L., Tang, Y., Dou, W., Chen, J.: Combining local optimization and enumeration for QoS-aware web service composition. In: 2010 International Conference on Web Services (ICWS), pp. 34–41 (2010)

    Google Scholar 

  13. Rao, J., Su, X.: A survey of automated web service composition methods. In: Cardoso, J., Sheth, A. (eds.) SWSWPC 2004. LNCS, vol. 3387, pp. 43–54. Springer, Heidelberg (2005). doi:10.1007/978-3-540-30581-1_5

    Chapter  Google Scholar 

  14. Rodriguez-Mier, P., Mucientes, M., Lama, M., Couto, M.I.: Composition of web services through genetic programming. Evol. Intell. 3(3–4), 171–186 (2010)

    Article  Google Scholar 

  15. Shet, K., Acharya, U.D., et al.: A new similarity measure for taxonomy based on edge counting (2012). arXiv preprint . arxiv:1211.4709

  16. da Silva, A.S., Ma, H., Zhang, M.: GraphEvol: a graph evolution technique for web service composition. In: Chen, Q., Hameurlain, A., Toumani, F., Wagner, R., Decker, H. (eds.) DEXA 2015. LNCS, vol. 9262, pp. 134–142. Springer, Cham (2015). doi:10.1007/978-3-319-22852-5_12

    Chapter  Google Scholar 

  17. da Silva, A.S., Ma, H., Zhang, M.: Genetic programming for QoS-aware web service composition and selection. Soft Comput. 20, 1–17 (2016)

    Article  Google Scholar 

  18. da Silva, A.S., Mei, Y., Ma, H., Zhang, M.: Particle swarm optimisation with sequence-like indirect representation for web service composition. In: Chicano, F., Hu, B., García-Sánchez, P. (eds.) EvoCOP 2016. LNCS, vol. 9595, pp. 202–218. Springer, Cham (2016). doi:10.1007/978-3-319-30698-8_14

    Chapter  Google Scholar 

  19. Wang, C., Ma, H., Chen, A., Hartmann, S.: Comprehensive quality-aware automated semantic web service composition. In: Peng, W., Alahakoon, D., Li, X. (eds.) AI 2017. LNCS, vol. 10400, pp. 195–207. Springer, Cham (2017). doi:10.1007/978-3-319-63004-5_16

    Chapter  Google Scholar 

  20. Yu, Y., Ma, H., Zhang, M.: An adaptive genetic programming approach to QoS-aware web services composition. In: 2013 IEEE Congress on Evolutionary Computation, pp. 1740–1747. IEEE (2013)

    Google Scholar 

  21. Zeng, L., Benatallah, B., Dumas, M., Kalagnanam, J., Sheng, Q.Z.: Quality driven web services composition. In: Proceedings of the 12th international conference on World Wide Web, pp. 411–421. ACM (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Victoria University of Wellington, Wellington, New Zealand

    Chen Wang, Hui Ma & Aaron Chen

  2. Clausthal University of Technology, Clausthal-zellerfeld, Germany

    Sven Hartmann

Authors
  1. Chen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hui Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aaron Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sven Hartmann
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chen Wang .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  2. City University of Hong Kong, Hong Kong, Kowloon, Hong Kong

    Kay Chen Tan

  3. Victoria University of Wellington, Wellington, Wellington, New Zealand

    Mengjie Zhang

  4. Southern University of Science and Technology, Shenzhen, China

    Ke Tang

  5. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

  6. City University of Hong Kong, Kowloon Tong, Hong Kong

    Qingfu Zhang

  7. Peking University, Beijing, China

    Ying Tan

  8. University of Leipzig, Leipzig, Germany

    Martin Middendorf

  9. University of Surrey, Guildford, Surrey, United Kingdom

    Yaochu Jin

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Wang, C., Ma, H., Chen, A., Hartmann, S. (2017). GP-Based Approach to Comprehensive Quality-Aware Automated Semantic Web Service Composition. In: Shi, Y., et al. Simulated Evolution and Learning. SEAL 2017. Lecture Notes in Computer Science(), vol 10593. Springer, Cham. https://doi.org/10.1007/978-3-319-68759-9_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-68759-9_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68758-2

  • Online ISBN: 978-3-319-68759-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation