Service Discovery from Observed Behavior while Guaranteeing Deadlock Freedom in Collaborations

  • Richard Müller
  • Christian Stahl
  • Wil M. P. van der Aalst
  • Michael Westergaard
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8274)


Process discovery techniques can be used to derive a process model from observed example behavior (i.e., an event log). As the observed behavior is inherently incomplete and models may serve different purposes, four competing quality dimensions—fitness, precision, simplicity, and generalization—have to be balanced to produce a process model of high quality.

In this paper, we investigate the discovery of processes that are specified as services. Given a service S and observed behavior of a service P interacting with S, we discover a service model of P. Our algorithm balances the four quality dimensions based on user preferences. Moreover, unlike existing discovery approaches, we guarantees that the composition of S and P is deadlock free. The service discovery technique has been implemented in ProM and experiments using service models of industrial size demonstrate the scalability or our approach.


State Machine Quality Dimension Service Composition Service Model Service Discovery 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    van der Aalst, W.M.P.: The application of Petri nets to workflow management. Journal of Circuits, Systems, and Computers 8(1), 21–66 (1998)CrossRefGoogle Scholar
  2. 2.
    van der Aalst, W.M.P.: Service mining: Using process mining to discover, check, and improve service behavior. IEEE Transactions on Services Computing (2012)Google Scholar
  3. 3.
    van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer (2011)Google Scholar
  4. 4.
    van der Aalst, W.M.P., Adriansyah, A., van Dongen, B.F.: Replaying history on process models for conformance checking and performance analysis. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2(2), 182–192 (2012)CrossRefGoogle Scholar
  5. 5.
    van der Aalst, W., et al.: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM Workshops 2011, Part I. LNBIP, vol. 99, pp. 169–194. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  6. 6.
    Adriansyah, A., Munoz-Gama, J., Carmona, J., van Dongen, B.F., van der Aalst, W.M.P.: Alignment based precision checking. In: La Rosa, M., Soffer, P. (eds.) BPM Workshops 2012. LNBIP, vol. 132, pp. 137–149. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  7. 7.
    Asbagh, M., Abolhassani, H.: Web service usage mining: mining for executable sequences. In: WSEAS 2007, vol. 7, pp. 266–271 (2007)Google Scholar
  8. 8.
    Basu, S., Casati, F., Daniel, F.: Toward web service dependency discovery for SOA management. In: SCC 2008, vol. 2, pp. 422–429 (2008)Google Scholar
  9. 9.
    Boender, C., Rinnooy Kan, A.: A bayesian analysis of the number of cells of a multinomial distribution. The Statistician, 240–248 (1983)Google Scholar
  10. 10.
    Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: On the role of fitness, precision, generalization and simplicity in process discovery. In: Meersman, R., et al. (eds.) OTM 2012, Part I. LNCS, vol. 7565, pp. 305–322. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  11. 11.
    Dustdar, S., Gombotz, R.: Discovering web service workflows using web services interaction mining. Int. Journal of Business Process Integration and Management 1(4), 256–266 (2006)CrossRefGoogle Scholar
  12. 12.
    Jordan, D., et al.: Web services business process execution language version 2.0. OASIS Standard 11 (2007)Google Scholar
  13. 13.
    Lohmann, N.: A feature-complete Petri net semantics for WS-BPEL 2.0. In: Dumas, M., Heckel, R. (eds.) WS-FM 2007. LNCS, vol. 4937, pp. 77–91. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  14. 14.
    Lohmann, N., Massuthe, P., Wolf, K.: Operating guidelines for finite-state services. In: Kleijn, J., Yakovlev, A. (eds.) ICATPN 2007. LNCS, vol. 4546, pp. 321–341. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  15. 15.
    Lohmann, N., Weinberg, D.: Wendy: A tool to synthesize partners for services. Fundam. Inform. 113(3-4), 295–311 (2011)MathSciNetGoogle Scholar
  16. 16.
    Medeiros, A., Weijters, A., van der Aalst, W.M.P.: Genetic process mining: an experimental evaluation. Data Mining and Knowledge Discovery 14, 245–304 (2007)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Mendling, J., Neumann, G., van der Aalst, W.M.P.: Understanding the occurrence of errors in process models based on metrics. In: Meersman, R., Tari, Z. (eds.) OTM 2007, Part I. LNCS, vol. 4803, pp. 113–130. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  18. 18.
    Motahari-Nezhad, H.R., Saint-Paul, R., Benatallah, B.: Deriving protocol models from imperfect service conversation logs. IEEE Trans. Knowl. Data Eng. 20(12), 1683–1698 (2008)CrossRefGoogle Scholar
  19. 19.
    Motahari Nezhad, H.R., Saint-Paul, R., Casati, F., Benatallah, B.: Event correlation for process discovery from web service interaction logs. The VLDB Journal 20(3), 417–444 (2010)CrossRefGoogle Scholar
  20. 20.
    Motahari-Nezhad, H., Saint-Paul, R., Benatallah, B., Casati, F.: Protocol discovery from imperfect service interaction logs. In: 2013 IEEE 29th International Conference on Data Engineering (ICDE), pp. 1405–1409 (2007)Google Scholar
  21. 21.
    Müller, R., van der Aalst, W.M.P., Stahl, C.: Conformance checking of services using the best matching private view. In: ter Beek, M.H., Lohmann, N. (eds.) WS-FM 2012. LNCS, vol. 7843, pp. 49–68. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  22. 22.
    Müller, R., Stahl, C., van der Aalst, W.M.P., Westergaard, M.: Service discovery from observed behavior while guaranteeing deadlock freedom in collaborations. BPM Center Report BPM-13-12, (2013),
  23. 23.
    Musaraj, K., Yoshida, T., Daniel, F., Hacid, M.S., Casati, F., Benatallah, B.: Message correlation and web service protocol mining from inaccurate logs. In: ICWS 2010, pp. 259–266 (2010)Google Scholar
  24. 24.
    Papazoglou, M.: Web Services - Principles and Technology. Prentice Hall (2008)Google Scholar
  25. 25.
    Rouached, M., Gaaloul, W., van der Aalst, W.M.P., Bhiri, S., Godart, C.: Web service mining and verification of properties: An approach based on event calculus. In: Meersman, R., Tari, Z. (eds.) OTM 2006. LNCS, vol. 4275, pp. 408–425. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  26. 26.
    Rozinat, A., van der Aalst, W.M.P.: Conformance checking of processes based on monitoring real behavior. Information Systems 33(1), 64–95 (2008)CrossRefGoogle Scholar
  27. 27.
    Tang, R., Zou, Y.: An approach for mining web service composition patterns from execution logs. In: WSE 2010, pp. 53–62 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Richard Müller
    • 1
    • 2
  • Christian Stahl
    • 2
  • Wil M. P. van der Aalst
    • 2
    • 3
  • Michael Westergaard
    • 2
    • 3
  1. 1.Institut für InformatikHumboldt-Universität zu BerlinGermany
  2. 2.Department of Mathematics and Computer ScienceTechnische Universiteit EindhovenThe Netherlands
  3. 3.National Research University Higher School of EconomicsMoscowRussia

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