Advertisement

Leveraging Process Mining on Service Events Towards Service Composition

  • Yulai LiEmail author
  • Hongming Cai
  • Chengxi Huang
  • Fenglin Bu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9464)

Abstract

Service composition is a widely-used approach in the development of applications. However, well-designed service composition approaches always lacks the consideration of execution environment, and the approach designed for application execution is usually incomplete and lacking necessary business consideration. In order to improve the comprehensiveness covered both design and execution stages, a service composition approach based on process mining is proposed. First, a meta-model is designed to connect the information of execution environment and business requirement. Next, the scene model based on this meta-model is generated by leveraging process mining. Then the scene model is applied to do service composition, including service selection from the Service Registry. After that, BPEL instance is converted based on aggregated scene information so as to enable application execution. Finally, a cloud-based logistics platform is implemented to verify the approach, and the result shows that the approach has high requirement accuracy and execution effectiveness.

Keywords

Service composition Service composition pattern Process mining Cloud computing 

Notes

Acknowledgement

We would like to acknowledge the anonymous reviewers for their insightful and constructive comments and the support of the National Natural Science Foundation of China under No. 71171132 and No. 61373030.

References

  1. 1.
    Aalst, W.V.D.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Gaaloul, W., Baïna, K., Godart, C.: Log-based Mining techniques applied to web service composition reengineering. In: Service Oriented Computing and Applications, vol. 2, no. 3, pp. 93–110. Springer, London (2008)Google Scholar
  3. 3.
    Wan, Z., Meng, F.J., Xu, J.M., Wang, P.: Service composition pattern generation for cloud migration: a graph similarity analysis approach. In: 21st IEEE International Conference on Web Services, pp. 321–328. IEEE Press (2014)Google Scholar
  4. 4.
    Moser, O., Rosenberg, F., Dustdar, S.: Event driven monitoring for service composition infrastructures. In: Chen, L., Triantafillou, P., Suel, T. (eds.) WISE 2010. LNCS, vol. 6488, pp. 38–51. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    Ahmed, T., Srivastava, A.: Minimizing waiting time for service composition: a frictional approach. In: 20th IEEE International Conference on Web Services, pp. 268–275. IEEE Press (2013)Google Scholar
  6. 6.
    Cui, L., Li, J., Zheng, Y.: A dynamic web service composition method based on viterbi algorithm. In: 19th IEEE International Conference on Web Services, pp. 267–271. IEEE Press (2012)Google Scholar
  7. 7.
    Alferez, G.H., Pelechano, V.: Facing uncertainty in web service compositions. In: 20th IEEE International Conference on Web Services, pp. 219–226. IEEE Press (2013)Google Scholar
  8. 8.
    Sirin, E., Parsia, B., Wu, D., Hendler, J., Nau, D.: HTN planning for web service composition using SHOP2. Web Semant. Sci. Serv. Agents World Wide Web 1(4), 377–396 (2004). ElsevierCrossRefGoogle Scholar
  9. 9.
    Cai, H., Cui, L., Shi, Y., Kong, L., Yan, Z.: Multi-tenant service composition based on granularity computing. In: 11th IEEE International Conference on Services Computing, pp. 669–676. IEEE Press (2014)Google Scholar
  10. 10.
    Aalst, W.V.D.: Service mining: using process mining to discover, check, and improve service behavior. In: IEEE Transactions on Services Computing, vol. 6, no. 4, pp. 525–535. IEEE Press (2013)Google Scholar
  11. 11.
    Rebug, Á., Ferreira, D.R.: Business process analysis in healthcare environments: a methodology based on process mining. Inf. Syst. 37(2), 99–116 (2012). Elsevier, OxfordCrossRefGoogle Scholar
  12. 12.
    Weijters, A.J.M.M., Aalst, W.M.P., Mans, R., Rozinat, A., Song, M., Dongen, B., et al.: Process mining with ProM. In: the 19th Belgium-Netherlands Conference on Artificial Intelligence (2007)Google Scholar
  13. 13.
    Chen, Y., Huang, J., Lin, C.: Partial selection: an efficient approach for QoS-aware web service composition. In: 21st IEEE International Conference on Web Services, pp. 1–8. IEEE Press (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Yulai Li
    • 1
    Email author
  • Hongming Cai
    • 1
  • Chengxi Huang
    • 1
  • Fenglin Bu
    • 1
  1. 1.School of SoftwareShanghai Jiao Tong UniversityShanghaiChina

Personalised recommendations