Merging Alignments for Decomposed Replay

  • H. M. W. VerbeekEmail author
  • W. M. P. van der Aalst
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9698)


In the area of process mining, conformance checking aims to find an optimal alignment between an event log (which captures the activities that actually have happened) and a Petri net (which describes expected or normative behavior). Optimal alignments highlight discrepancies between observed and modeled behavior. To find an optimal alignment, a potentially challenging optimization problem needs to be solved based on a predefined cost function for misalignments. Unfortunately, this may be very time consuming for larger logs and models and often intractable. A solution is to decompose the problem of finding an optimal alignment in many smaller problems that are easier to solve. Decomposition can be used to detect conformance problems in less time and provides a lower bound for the costs of an optimal alignment. Although the existing approach is able to decide whether a trace fits or not, it does not provide an overall alignment. In this paper, we provide an algorithm that is able to provide such an optimal alignment from the decomposed alignments if this is possible. Otherwise, the algorithm produces a so-called pseudo-alignment that can still be used to pinpoint non-conforming parts of log and model. The approach has been implemented in ProM and tested on various real-life event logs.


  1. 1.
    van der Aalst, W.M.P.: Decomposing Petri Nets for process mining: a generic approach. Distrib. Parallel Databases 31(4), 471–507 (2013). Scholar
  2. 2.
    van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes, 1st edn. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    van der Aalst, W.M.P., Adriansyah, A., van Dongen, B.F.: Replaying history on process models for conformance checking and performance analysis. Wiley Interdisc. Rev.: Data Min. Knowl. Discov. 2(2), 182–192 (2012). Scholar
  4. 4.
    Adriansyah, A., van Dongen, B.F, van der Aalst, W.M.P.: Conformance checking using cost-based fitness analysis. In: Proceedings of the 2011 IEEE 15th International Enterprise Distributed Object Computing Conference, EDOC 2011, pp. 55–64. IEEE Computer Society, Washington, DC (2011).
  5. 5.
    Adriansyah, A., Sidorova, N., van Dongen, B.F.: Cost-based fitness in conformance checking. In: 2011 11th International Conference on Application of Concurrency to System Design (ACSD), pp. 57–66, June 2011Google Scholar
  6. 6.
    Berthelot, G.: Transformations and decompositions of nets. In: Brauer, W., Reisig, W., Rozenberg, G. (eds.) Advances in Petri Nets 1986, Part I. LNCS, vol. 254, pp. 360–376. Springer, Heidelberg (1987)Google Scholar
  7. 7.
    vanden Broucke, S.K.L.M., De Weerdt, J., Vanthienen, J., Baesens, B.: Determining process model precision and generalization with weighted artificial negative events. IEEE Trans. Knowl. Data Eng. 26(8), 1877–1889 (2014)CrossRefGoogle Scholar
  8. 8.
    Calders, T., Günther, C.W., Pechenizkiy, M., Rozinat, A.: Using minimum description length for process mining. In: Proceedings of the 2009 ACM Symposium on Applied Computing, SAC 2009, pp. 1451–1455. ACM, New York (2009).
  9. 9.
    Cook, J.E., Wolf, A.L.: Software process validation: quantitatively measuring the correspondence of a process to a model. ACM Trans. Softw. Eng. Methodol. 8(2), 147–176 (1999). Scholar
  10. 10.
    De Weerdt, J., De Backer, M., Vanthienen, J., Baesens, B.: A robust f-measure for evaluating discovered process models. In: 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 148–155, April 2011Google Scholar
  11. 11.
    Goedertier, S., Martens, D., Vanthienen, J., Baesens, B.: Robust process discovery with artificial negative events. J. Mach. Learn. Res. 10, 1305–1340 (2009). Scholar
  12. 12.
    Maruster, L., Weijters, A.J.M.M., van der Aalst, W.M.P., Van Den Bosch, A.: A rule-based approach for process discovery: dealing with noise and imbalance in process logs. Data Min. Knowl. Disc. 13(1), 67–87 (2006). Scholar
  13. 13.
    Muñoz-Gama, J., Carmona, J.: A fresh look at precision in process conformance. In: Hull, R., Mendling, J., Tai, S. (eds.) BPM 2010. LNCS, vol. 6336, pp. 211–226. Springer, Heidelberg (2010). Scholar
  14. 14.
    Muñoz-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
  15. 15.
    Muñoz-Gama, J., Carmona, J., van der Aalst, W.M.P.: Single-entry single-exit decomposed conformance checking. Inf. Syst. 46, 102–122 (2014). Scholar
  16. 16.
    Murata, T.: Petri nets: properties, analysis and applications. Proc. IEEE 77(4), 541–580 (1989)CrossRefGoogle Scholar
  17. 17.
    Rozinat, A., van der Aalst, W.M.P.: Conformance checking of processes based on monitoring real behavior. Inf. Syst. 33(1), 64–95 (2008). Scholar
  18. 18.
    Verbeek, H.M.W.: Decomposed process mining with divide-and-conquer. In: BPM 2014 Demos, vol. 1295, pp. 86–90. (2014).
  19. 19.
    Verbeek, H.M.W., Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P: ProM 6: the process mining toolkit. In: Proceedings of BPM Demonstration Track 2010, vol. 615, pp. 34–39. (2010).

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Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceEindhoven University of TechnologyEindhovenThe Netherlands

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