Event-Based Real-Time Decomposed Conformance Analysis

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8841)


Process mining deals with the extraction of knowledge from event logs. One important task within this research field is denoted as conformance checking, which aims to diagnose deviations and discrepancies between modeled behavior and real-life, observed behavior. Conformance checking techniques still face some challenges, among which scalability, timeliness and traceability issues. In this paper, we propose a novel conformance analysis methodology to support the real-time monitoring of event-based data streams, which is shown to be more efficient than related approaches and able to localize deviations in a more fine-grained manner. Our developed approach can be directly applied in business process contexts where rapid reaction times are crucial; an exhaustive case example is provided to evidence the validity of the approach.


real-time monitoring process decomposition conformance checking conformance analysis process mining event logs 


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  1. 1.
    van der Aalst, W.M.P.: Decomposing Petri nets for process mining: A generic approach. Distributed and Parallel Databases 31(4), 471–507 (2013)CrossRefGoogle Scholar
  2. 2.
    Adriansyah, A., Muñoz-Gama, J., Carmona, J.: Alignment Based Precision Checking. In: BPM Center Report, 12-10-2012 (2012)Google Scholar
  3. 3.
    Adriansyah, A., van Dongen, B.F., van der Aalst, W.M.P.: Towards Robust Conformance Checking. In: Muehlen, M.z., Su, J. (eds.) BPM 2010 Workshops. LNBIP, vol. 66, pp. 122–133. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Adriansyah, A., van Dongen, B.F.: Conformance checking using cost-based fitness analysis. In: Proceedings of the 15th IEEE International Enterprise Distributed Object Computing Conference (EDOC 2011), pp. 55–64 (2011)Google Scholar
  5. 5.
    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. Lecture Notes in Business Information Processing, vol. 132, pp. 137–149. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  6. 6.
    van der Aalst, W., Adriansyah, A.: Replaying history on process models for conformance checking and performance analysis. WIREs Data Mining and Knowledge Discovery 2, 1–18 (2012)CrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    Weidlich, M., Mendling, J., Weske, M.: Efficient consistency measurement based on behavioral profiles of process models. IEEE Trans. Software Eng. 37(3), 410–429 (2011)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Smirnov, S., Weidlich, M., Mendling, J.: Business process model abstraction based on behavioral profiles. In: Maglio, P.P., Weske, M., Yang, J., Fantinato, M. (eds.) ICSOC 2010. LNCS, vol. 6470, pp. 1–16. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    van der Aalst, W.M.P.: Decomposing process mining problems using passages. In: Haddad, S., Pomello, L. (eds.) PETRI NETS 2012. LNCS, vol. 7347, pp. 72–91. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  11. 11.
    Munoz-Gama, J., Carmona, J., van der Aalst, W.M.P.: Conformance checking in the large: Partitioning and topology. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 130–145. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  12. 12.
    Munoz-Gama, J., Carmona, J., van der Aalst, W.M.P.: Hierarchical conformance checking of process models based on event logs. In: Colom, J.-M., Desel, J. (eds.) PETRI NETS 2013. LNCS, vol. 7927, pp. 291–310. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  13. 13.
    Gal, A., Hadar, E.: Generic architecture of complex event processing systems. In: Hinze, A., Buchmann, A.P. (eds.) Principles and Applications of Distributed Event-Based Systems, pp. 1–18. IGI Global (2010)Google Scholar
  14. 14.
    Zarri, G.P.: Representation and processing of complex events. In: AAAI Spring Symposium: Intelligent Event Processing, p. 101. AAAI (2009)Google Scholar
  15. 15.
    Kang, B., Lee, S.K., Bin Min, Y., Kang, S.H., Cho, N.W.: Real-time process quality control for business activity monitoring. In: Gavrilova, M.L., Gervasi, O., Taniar, D., Mun, Y., Iglesias, A. (eds.) ICCSA Workshops, pp. 237–242. IEEE Computer Society (2009)Google Scholar
  16. 16.
    Janiesch, C., Matzner, M., Müller, O.: Beyond process monitoring: a proof-of-concept of event-driven business activity management. Business Proc. Manag. Journal 18(4), 625–643 (2012)CrossRefGoogle Scholar
  17. 17.
    Murata, T.: Petri nets: Properties, analysis and applications. Proceedings of the IEEE 77(4), 541–580 (1989)CrossRefGoogle Scholar
  18. 18.
    Polyvyanyy, A., Vanhatalo, J., Völzer, H.: Simplified computation and generalization of the refined process structure tree. In: Bravetti, M., Bultan, T. (eds.) WS-FM 2010. LNCS, vol. 6551, pp. 25–41. Springer, Heidelberg (2011)Google Scholar
  19. 19.
    vanden Broucke, S.K., Weerdt, J.D., Vanthienen, J., Baesens, B.: Determining process model precision and generalization with weighted artificial negative events. IEEE Transactions on Knowledge and Data Engineering 99(PrePrints), 1 (2013)Google Scholar
  20. 20.
    vanden Broucke, S., De Weerdt, J., Vanthienen, J., Baesens, B.: On replaying process execution traces containing positive and negative events. Feb research report kbi 1311, KU Leuven (2013)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Department of Decision Sciences and Information ManagementKU LeuvenLeuvenBelgium
  2. 2.Universitat Politecnica de CatalunyaBarcelonaSpain
  3. 3.School of ManagementUniversity of SouthamptonHighfield SouthamptonUnited Kingdom

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