Estimating Process Conformance by Trace Sampling and Result Approximation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11675)


The increasing volume of event data that is recorded by information systems during the execution of business processes creates manifold opportunities for process analytics. Specifically, conformance checking compares the behaviour as recorded by an information system to a model of desired behaviour. Unfortunately, state-of-the-art conformance checking algorithms scale exponentially in the size of both the event data and the model used as input. At the same time, event data used for analysis typically relates only to a certain interval of process execution, not the entire history. Given this inherent data incompleteness, we argue that an understanding of the overall conformance of process execution may be obtained by considering only a small fraction of a log. In this paper, we therefore present a statistical approach to ground conformance checking in trace sampling and conformance approximation. This approach reduces the runtime significantly, while still providing guarantees on the accuracy of the estimated conformance result. Comprehensive experiments with real-world and synthetic datasets illustrate that our approach speeds up state-of-the-art conformance checking algorithms by up to three orders of magnitude, while largely maintaining the analysis accuracy.


  1. 1.
    Van der Aalst, W.M.P.: Process Mining - Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011). Scholar
  2. 2.
    Aalst, W.M.P.: Data scientist: the engineer of the future. In: Mertins, K., Bénaben, F., Poler, R., Bourrières, J.-P. (eds.) Enterprise Interoperability VI. PIC, vol. 7, pp. 13–26. Springer, Cham (2014). Scholar
  3. 3.
    der Aalst, W.M.P.V., Verbeek, H.M.W.: Process discovery and conformance checking using passages. Fundam. Inform. 131(1), 103–138 (2014)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Adriansyah, A., van Dongen, B., Van der Aalst, W.M.P.: Conformance checking using cost-based fitness analysis. In: EDOC, pp. 55–64 (2011)Google Scholar
  5. 5.
    Bauer, M., Senderovich, A., Gal, A., Grunske, L., Weidlich, M.: How much event data is enough? A statistical framework for process discovery. In: CAiSE, pp. 239–256 (2018)Google Scholar
  6. 6.
    Biermann, A.W., Feldman, J.A.: On the synthesis of finite-state machines from samples of their behavior. IEEE Trans. Comput. 100(6), 592–597 (1972)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Busany, N., Maoz, S.: Behavioral log analysis with statistical guarantees. In: ICSE, pp. 877–887. ACM (2016)Google Scholar
  8. 8.
    Carmona, J., Cortadella, J.: Process mining meets abstract interpretation. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010. LNCS (LNAI), vol. 6321, pp. 184–199. Springer, Heidelberg (2010). Scholar
  9. 9.
    Carmona, J., van Dongen, B., Solti, A., Weidlich, M.: Conformance Checking - Relating Processes and Models. Springer, Cham (2018). Scholar
  10. 10.
    Cohen, H., Maoz, S.: Have we seen enough traces? In: ASE, pp. 93–103. IEEE (2015)Google Scholar
  11. 11.
    De Leoni, M.M., Mannhardt, F.F.: Road traffic fine management process (2015).
  12. 12.
    Dixit, P.M., Buijs, J.C.A.M., Verbeek, H.M.W., van der Aalst, W.M.P.: Fast incremental conformance analysis for interactive process discovery. In: Abramowicz, W., Paschke, A. (eds.) BIS 2018. LNBIP, vol. 320, pp. 163–175. Springer, Cham (2018). Scholar
  13. 13.
    Dongen, B.F.: Efficiently computing alignments - using the extended marking equation. In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds.) BPM 2018. LNCS, vol. 11080, pp. 197–214. Springer, Cham (2018). Scholar
  14. 14.
    van Dongen, B., Carmona, J., Chatain, T., Taymouri, F.: Aligning modeled and observed behavior: a compromise between computation complexity and quality. In: Dubois, E., Pohl, K. (eds.) CAiSE 2017. LNCS, vol. 10253, pp. 94–109. Springer, Cham (2017). Scholar
  15. 15.
    Dumas, M., Rosa, M.L., Mendling, J., Reijers, H.A.: Fundamentals of Business Process Management, 2nd edn. Springer, Heidelberg (2018). Scholar
  16. 16.
    Evermann, J.: Scalable process discovery using map-reduce. IEEE TSC 9(3), 469–481 (2016)Google Scholar
  17. 17.
    van Hee, K.M., Liu, Z., Sidorova, N.: Is my event log complete? - a probabilistic approach to process mining. In: International Conference on Research Challenges in Information Science, pp. 1–7 (2011)Google Scholar
  18. 18.
    Lee, W.L.J., Verbeek, H.M.W., Munoz-Gama, J., der Aalst, W.M.P.V., Sepúlveda, M.: Recomposing conformance: closing the circle on decomposed alignment-based conformance checking in process mining. Inf. Sci. 466, 55–91 (2018)CrossRefGoogle Scholar
  19. 19.
    Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs containing infrequent behaviour. In: Lohmann, N., Song, M., Wohed, P. (eds.) BPM 2013. LNBIP, vol. 171, pp. 66–78. Springer, Cham (2014). Scholar
  20. 20.
    Leemans, S.J.J., Fahland, D., der Aalst, W.M.P.V.: Scalable process discovery and conformance checking. Softw. Syst. Model. 2, 599–631 (2018)CrossRefGoogle Scholar
  21. 21.
    de Leoni, M., Marrella, A.: How planning techniques can help process mining: the conformance-checking case. In: Italian Symposium on Advanced Database Systems, p. 283 (2017)Google Scholar
  22. 22.
    Luo, C., He, F., Ghezzi, C.: Inferring software behavioral models with MapReduce. Sci. Comput. Program. 145, 13–36 (2017)CrossRefGoogle Scholar
  23. 23.
    Munoz-Gama, J., Carmona, J., van der Aalst, W.: Single-entry single-exit decomposed conformance checking. Inf. Syst. 46, 102–122 (2014)CrossRefGoogle Scholar
  24. 24.
    (Jorge) Munoz-Gama, J.: Conformance checking in the large (dataset) (2013).
  25. 25.
    Reißner, D., Conforti, R., Dumas, M., Rosa, M.L., Armas-Cervantes, A.: Scalable conformance checking of business processes. In: Panetto, H., et al. (eds.) OTM 2017. LNCS, vol. 10573, pp. 607–627. Springer, Cham (2017). Scholar
  26. 26.
    Rogge-Solti, A., Senderovich, A., Weidlich, M., Mendling, J., Gal, A.: In log and model we trust? A generalized conformance checking framework. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNCS, vol. 9850, pp. 179–196. Springer, Cham (2016). Scholar
  27. 27.
    Rozinat, A., Van der Aalst, W.M.P.: Conformance checking of processes based on monitoring real behavior. Inf. Syst. 33(1), 64–95 (2008)CrossRefGoogle Scholar
  28. 28.
    Taymouri, F., Carmona, J.: Model and event log reductions to boost the computation of alignments. In: Ceravolo, P., Guetl, C., Rinderle-Ma, S. (eds.) SIMPDA 2016. LNBIP, vol. 307, pp. 1–21. Springer, Cham (2018). Scholar
  29. 29.
    Taymouri, F., Carmona, J.: A recursive paradigm for aligning observed behavior of large structured process models. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNCS, vol. 9850, pp. 197–214. Springer, Cham (2016). Scholar
  30. 30.
    Taymouri, F., Carmona, J.: An evolutionary technique to approximate multiple optimal alignments. In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds.) BPM 2018. LNCS, vol. 11080, pp. 215–232. Springer, Cham (2018). Scholar
  31. 31.
    Van Dongen, B.: BPI Challenge 2012 (2012).
  32. 32.
    Van Dongen, B.: BPI Challenge 2014 (2014).
  33. 33.
    Verbeek, E., Buijs, J.C.A.M., van Dongen, B.F., der Aalst, W.M.P.V.: ProM 6: the process mining toolkit. In: Business Process Management (Demonstration Track) (2010)Google Scholar
  34. 34.
    Weber, B., Reichert, M., Rinderle-Ma, S.: Change patterns and change support features - enhancing flexibility in process-aware information systems. DKE 66(3), 438–466 (2008)CrossRefGoogle Scholar
  35. 35.
    Weidlich, M., Polyvyanyy, A., Desai, N., Mendling, J., Weske, M.: Process compliance analysis based on behavioural profiles. Inf. Syst. 36(7), 1009–1025 (2011)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceHumboldt-Universität zu BerlinBerlinGermany

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