Advertisement

Process Model Realism: Measuring Implicit Realism

  • Benoît DepaireEmail author
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 202)

Abstract

Determining the quality of a discovered process model is an important but non-trivial task. In this article, we focus on evaluating the realism level of a discovered process model, i.e. to what extent does the model contain the process behavior that is present in the true underlying process and nothing more. The IR Measure is proposed which represents the probability that a discovered model would have produced a log that is missing a certain amount of behavior observed in the discovered model. This measure expresses the strength of evidence that the discovered process model could be the true underlying model. Empirical results show that the Measure behaves as expected. The IR value drops when the discovered model contains unrealistic behavior. The IR value decreases as the amount of unrealistic behavior in the discovered model increases. The IR value increases as the amount of behavior in the underlying process increases, ceteris paribus.

Keywords

Process model quality Process model realism Implicit realism measure 

References

  1. 1.
    van der Aalst, W., Weijters, T., Maruster, L.: Workflow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004)CrossRefGoogle Scholar
  2. 2.
    Rozinat, A., de Medeiros, A.A., Günther, C.W., Weijters, A., van der Aalst, W.M.: Towards an evaluation framework for process mining algorithms. Beta, Research School for Operations Management and Logistics (2007)Google Scholar
  3. 3.
    De Weerdt, J., De Backer, M., Vanthienen, J., Baesens, B.: A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs. Inf. Syst. 37(7), 654–676 (2012)CrossRefGoogle Scholar
  4. 4.
    Buijs, J., van Dongen, B.F., van der Aalst, W.M.P.: Discovering and navigating a collection of process models using multiple quality dimensions. In: Ninth International Workshop on Business Process Intelligence (2013)Google Scholar
  5. 5.
    Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs - a constructive approach. In: Colom, J.-M., Desel, J. (eds.) PETRI NETS 2013. LNCS, vol. 7927, pp. 311–329. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  6. 6.
    van der Aalst, W., Adriansyah, A., van Dongen, B.: Replaying history on process models for conformance checking and performance analysis. Wiley Interdisc. Rev. Data Min. Knowl. Disc. 2(2), 182–192 (2012)CrossRefGoogle Scholar
  7. 7.
    Rozinat, A., van der Aalst, W.M.: Conformance checking of processes based on monitoring real behavior. Inf. Syst. 33(1), 64–95 (2008)CrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    Fisher, R.A.: Statistical Methods For Research Workers. Cosmo Publications, New Delhi (1925)Google Scholar
  10. 10.
    Fisher, R.A.: Statistical methods and scientific inference. Hafner Press, New York (1973)zbMATHGoogle Scholar
  11. 11.
    Adriansyah, A., Munoz-Gama, J., Carmona, J., van Dongen, B.F., van der Aalst, W.M.P.: Measuring precision of modeled behavior. Inf. Syst. e-Bus. Manag. 13, 1–31 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of Business EconomicsHasselt UniversityDiepenbeekBelgium
  2. 2.Research Foundation - Flanders (FWO)BrusselsBelgium

Personalised recommendations