Discovering and Exploring State-Based Models for Multi-perspective Processes

  • Maikel L. van Eck
  • Natalia Sidorova
  • Wil M. P. van der Aalst
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9850)

Abstract

Process mining provides fact-based insights into process behaviour captured in event data. In this work we aim to discover models for processes where different facets, or perspectives, of the process can be identified. Instead of focussing on the events or activities that are executed in the context of a particular process, we concentrate on the states of the different perspectives and discover how they are related. We present a formalisation of these relations and an approach to discover state-based models highlighting them. The approach has been implemented using the process mining framework ProM and provides a highly interactive visualisation of the multi-perspective state-based models. This tool has been evaluated on the BPI Challenge 2012 data of a loan application process and on product user behaviour data gathered by Philips during the development of a smart baby bottle equipped with various sensors.

References

  1. 1.
    van der Aalst, W.M.P., Rubin, V., Verbeek, H.M.W., van Dongen, B.F., Kindler, E., Günther, C.W.: Process mining: a two-step approach to balance between underfitting and overfitting. Softw. Syst. Model. 9(1), 87–111 (2010)CrossRefGoogle Scholar
  2. 2.
    Baier, C., Katoen, J.: Principles of Model Checking. MIT Press, Cambridge (2008)MATHGoogle Scholar
  3. 3.
    Beschastnikh, I., Brun, Y., Ernst, M.D., Krishnamurthy, A.: Inferring models of concurrent systems from logs of their behavior with CSight. In: 36th International Conference on Software Engineering, ICSE 2014, pp. 468–479 (2014)Google Scholar
  4. 4.
    Biermann, A.W., Feldman, J.A.: On the synthesis of finite-state machines from samples of their behavior. IEEE Trans. Comput. 21(6), 592–597 (1972)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Cook, J.E., Wolf, A.L.: Discovering models of software processes from event-based data. ACM Trans. Softw. Eng. Methodol. 7(3), 215–249 (1998)CrossRefGoogle Scholar
  6. 6.
    van Dongen, B.F.: BPI Challenge 2012 (2012). http://dx.doi.org/10.4121/uuid:3926db30-f712-4394-aebc-75976070e91f
  7. 7.
    Fisler, K., Krishnamurthi, S.: Modular verification of collaboration-based software designs. In: Proceedings of 8th European Software Engineering Conference 2001, pp. 152–163 (2001)Google Scholar
  8. 8.
    Gransden, T., Walkinshaw, N., Raman, R.: Mining state-based models from proof corpora. In: Watt, S.M., Davenport, J.H., Sexton, A.P., Sojka, P., Urban, J. (eds.) CICM 2014. LNCS, vol. 8543, pp. 282–297. Springer, Heidelberg (2014)Google Scholar
  9. 9.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, New York (2001)CrossRefMATHGoogle Scholar
  10. 10.
    Kam, T., Villa, T., Brayton, R.K., Sangiovanni-Vincentelli, A.: Synthesis of Finite State Machines: Functional Optimization. Springer Science and Business Media, New York (2013)MATHGoogle Scholar
  11. 11.
    Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Exploring processes and deviations. In: Fournier, F., Mendling, J. (eds.) BPM 2014 Workshops. LNBIP, vol. 202, pp. 304–316. Springer, Heidelberg (2015)Google Scholar
  12. 12.
    Lorenzoli, D., Mariani, L., Pezzè, M.: Automatic generation of software behavioral models. In: 30th International Conference on Software Engineering (ICSE 2008), pp. 501–510 (2008)Google Scholar
  13. 13.
    Lu, X., Nagelkerke, M., van de Wiel, D., Fahland, D.: Discovering interacting artifacts from ERP systems. IEEE Trans. Serv. Comput. 8(6), 861–873 (2015)CrossRefGoogle Scholar
  14. 14.
    Popova, V., Fahland, D., Dumas, M.: Artifact lifecycle discovery. Int. J. Coop. Inf. Syst. 24(1), 144 (2015)CrossRefGoogle Scholar
  15. 15.
    Ryndina, K., Küster, J.M., Gall, H.C.: Consistency of business process models and object life cycles. In: Kühne, T. (ed.) MoDELS 2006. LNCS, vol. 4364, pp. 80–90. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  16. 16.
    Weijters, A.J.M.M., Ribeiro, J.T.S.: Flexible heuristics miner (FHM). In: Proceedings of IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2011, pp. 310–317 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Maikel L. van Eck
    • 1
  • Natalia Sidorova
    • 1
  • Wil M. P. van der Aalst
    • 1
  1. 1.Eindhoven University of TechnologyEindhovenThe Netherlands

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