Discovering Process Models from Uncertain Event Data

  • Marco PegoraroEmail author
  • Merih Seran Uysal
  • Wil M. P. van der Aalst
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 362)


Modern information systems are able to collect event data in the form of event logs. Process mining techniques allow to discover a model from event data, to check the conformance of an event log against a reference model, and to perform further process-centric analyses. In this paper, we consider uncertain event logs, where data is recorded together with explicit uncertainty information. We describe a technique to discover a directly-follows graph from such event data which retains information about the uncertainty in the process. We then present experimental results of performing inductive mining over the directly-follows graph to obtain models representing the certain and uncertain part of the process.


Process mining Process discovery Uncertain data 


  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.
    Van der Aalst, W.M.: Process Mining: Data Science in Action. Springer, Heidelberg (2016). Scholar
  3. 3.
    Berti, A., van Zelst, S.J., van der Aalst, W.: Process mining for Python (PM4Py): bridging the gap between process- and data science. In: International Conference on Process Mining - Demo Track. IEEE (2019)Google Scholar
  4. 4.
    Conforti, R., La Rosa, M., ter Hofstede, A.H.: Filtering out infrequent behavior from business process event logs. IEEE Trans. Knowl. Data Eng. 29(2), 300–314 (2017)CrossRefGoogle Scholar
  5. 5.
    Hornik, K., Grün, B., Hahsler, M.: arules - a computational environment for mining association rules and frequent item sets. J. Stat. Softw. 14(15), 1–25 (2005)Google Scholar
  6. 6.
    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). Scholar
  7. 7.
    Leemans, S.J., Fahland, D., Van der Aalst, W.M.: Scalable process discovery and conformance checking. Softw. Syst. Model. 17(2), 599–631 (2018)CrossRefGoogle Scholar
  8. 8.
    Lu, X., Fahland, D., van den Biggelaar, F.J.H.M., van der Aalst, W.M.P.: Detecting deviating behaviors without models. In: Reichert, M., Reijers, H.A. (eds.) BPM 2015. LNBIP, vol. 256, pp. 126–139. Springer, Cham (2016). Scholar
  9. 9.
    Pegoraro, M., van der Aalst, W.M.: Mining uncertain event data in process mining. In: International Conference on Process Mining. IEEE (2019)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Process and Data Science Group (PADS), Department of Computer ScienceRWTH Aachen UniversityAachenGermany

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