Process Discovery Using Localized Events

  • Wil M. P. van der AalstEmail author
  • Anna Kalenkova
  • Vladimir Rubin
  • Eric Verbeek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9115)


Process mining techniques aim to analyze and improve conformance and performance of processes using event data. Process discovery is the most prominent process-mining task: A process model is derived based on an event log. The process model should be able to capture causalities, choices, concurrency, and loops. Process discovery is very challenging because of trade-offs between fitness, simplicity, precision, and generalization. Note that event logs typically only hold example behavior and cannot be assumed to be complete (to avoid overfitting). Dozens of process discovery techniques have been proposed. These use a wide range of approaches, e.g., language- or state-based regions, genetic mining, heuristics, expectation maximization, iterative log-splitting, etc. When models or logs become too large for analysis, the event log may be automatically decomposed or traces may be clustered before discovery. Clustering and decomposition are done automatically, i.e., no additional information is used. This paper proposes a different approach where a localized event log is assumed. Events are localized by assigning a non-empty set of regions to each event. It is assumed that regions can only interact through shared events. Consider for example the mining of software systems. The events recorded typically explicitly refer to parts of the system (components, services, etc.). Currently, such information is ignored during discovery. However, references to system parts may be used to localize events. Also in other application domains, it is possible to localize events, e.g., communication events in an organization may refer to multiple departments (that may be seen as regions). This paper proposes a generic process discovery approach based on localized event logs. The approach has been implemented in ProM and experimental results show that location information indeed helps to improve the quality of the discovered models.


Visible Trace Conformance Check Vertical Decomposition Inductive Miner Unique Trace 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    van der Aalst, W.M.P.: Process Mining: Discovery. Conformance and Enhancement of Business Processes. Springer, Berlin (2011)Google Scholar
  2. 2.
    van der Aalst, W.M.P.: Distributed Process Discovery and Conformance Checking. In: de Lara, J., Zisman, A., (eds.) FASE 2012. LNCS, vol. 7212, pp. 1–25. Springer, Heidelberg (2012)Google Scholar
  3. 3.
    van der Aalst, W.M.P.: A General Divide and Conquer Approach for Process Mining. In: Federated Conference on Computer Science and Information Systems (FedCSIS 2013), pp. 1–10. IEEE Computer Society (2013)Google Scholar
  4. 4.
    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
  5. 5.
    van der Aalst, W.M.P., Rubin, V., Verbeek, H.M.W., van Dongen, B.F., Kindler, E., Günther, C.W.: Process Mining: A Two-Step Approach to Balance Between Underfitting and Overfitting. Software and Systems Modeling 9(1), 87–111 (2010)CrossRefGoogle Scholar
  6. 6.
    van der Aalst, W.M.P., Weijters, A.J.M.M., Maruster, L.: Workflow Mining: Discovering Process Models from Event Logs. IEEE Transactions on Knowledge and Data Engineering 16(9), 1128–1142 (2004)CrossRefGoogle Scholar
  7. 7.
    Agrawal, R., Gunopulos, D., Leymann, F.: Mining Process Models from Workflow Logs. In: Schek, H.-J., Alonso, G., Saltor, F., Ramos, I. (eds.) EDBT 1998. LNCS, vol. 1377, pp. 469–483. Springer, Berlin (1998)Google Scholar
  8. 8.
    Bergenthum, R., Desel, J., Lorenz, R., Mauser, S.: Process Mining Based on Regions of Languages. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) International Conference on Business Process Management (BPM 2007). Lecture Notes in Computer Science, vol. 4714, pp. 375–383. Springer-Verlag, Berlin (2007)Google Scholar
  9. 9.
    Bose, R.P.J.C., van der Aalst, W.M.P.: Trace Clustering Based on Conserved Patterns: Towards Achieving Better Process Models. In: Rinderle-Ma, S., Sadiq, S., Leymann, F. (eds.) PM 2009 Workshops. LNBIP, vol. 43, pp. 170–181. Springer, Heidelberg (2010)Google Scholar
  10. 10.
    Carmona, J., Cortadella, J.: Process Mining Meets Abstract Interpretation. In: Balcazar, J.L. (ed.) ECML/PKDD 210. Lecture Notes in Artificial Intelligence, vol. 6321, pp. 184–199. Springer-Verlag, Berlin (2010)Google Scholar
  11. 11.
    Carmona, J., Cortadella, J., Kishinevsky, M.: A Region-Based Algorithm for Discovering Petri Nets from Event Logs. Business Process Management 2008, 358–373 (2008)Google Scholar
  12. 12.
    Carmona, J., Cortadella, J., Kishinevsky, M.: Divide-and-Conquer Strategies for Process Mining. In: Dayal, U., Eder, J., Koehler, J., Reijers, H.A. et al. (eds.) BPM 2009. LNCS, vol. 5701, pp. 327–343. Springer, Heidelberg (2009)Google Scholar
  13. 13.
    Cook, J.E., Wolf, A.L.: Discovering Models of Software Processes from Event-Based Data. ACM Transactions on Software Engineering and Methodology 7(3), 215–249 (1998)CrossRefGoogle Scholar
  14. 14.
    Darondeau, P.: Unbounded Petri Net Synthesis. In: Desel, J., Reisig, W., Rozenberg, G. (eds.) ACPN 2003. LNCS, vol. 3098, pp. 413–438. Springer, Heidelberg (2004)Google Scholar
  15. 15.
    Goedertier, S., Martens, D., Vanthienen, J., Baesens, B.: Robust Process Discovery with Artificial Negative Events. Journal of Machine Learning Research 10, 1305–1340 (2009)zbMATHMathSciNetGoogle Scholar
  16. 16.
    Greco, G., Guzzo, A., Pontieri, L., Saccà, D.: Discovering Expressive Process Models by Clustering Log Traces. IEEE Transaction on Knowledge and Data Engineering 18(8), 1010–1027 (2006)CrossRefGoogle Scholar
  17. 17.
    Kalenkova, A., de Leoni, M., van der Aalst, W.M.P.: Discovering, Analyzing and Enhancing BPMN Models Using ProM. In: Business Process Management Demo Sessions (BPMD 2014), vol. 1295. CEUR Workshop Proceedings, pp. 36–40 (2014)Google Scholar
  18. 18.
    Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering Block-Structured Process Models from Event Logs Containing Infrequent Behaviour. In: Song, M., Wohed, P. et al. (eds.): BPM 2013 Workshops. LNBIP, vol. 171, pp. 66–78, 2014. Springer, Heidelberg (2014)Google Scholar
  19. 19.
    Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering Block-structured Process Models from Incomplete Event Logs. In: Ciardo, G., Kindler, E. (eds.) PETRI NETS 2014. LNCS, vol. 8489, pp. 91–110. Springer-Verlag, Berlin (2014)Google Scholar
  20. 20.
    Mazurkiewicz, A.: Semantics of Concurrent Systems: A Modular Fixed-Point Trace Approach. In: Rozenberg, G. (ed.) Advances in Petri Nets 1984. Lecture Notes in Computer Science, vol. 188, pp. 353–375. Springer-Verlag, Berlin (1984)CrossRefGoogle Scholar
  21. 21.
    Alves de Medeiros, A.K., Weijters, A.J.M.M., van der Aalst, W.M.P.: Genetic Process Mining: An Experimental Evaluation. Data Mining and Knowledge Discovery 14(2), 245–304 (2007)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Munoz-Gama, J., Carmona, J., van der Aalst, W.M.P.: Single-Entry Single-Exit Decomposed Conformance Checking. Information Systems 46, 102–122 (2014)CrossRefGoogle Scholar
  23. 23.
    OMG. Business Process Model and Notation (BPMN). Object Management Group, formal/2011-01-03 (2011)Google Scholar
  24. 24.
    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. Springr, Heidelberg (2011)Google Scholar
  25. 25.
    Sole, M., Carmona, J.: Process Mining from a Basis of Regions. In: Lilius, J., Penczek, W. (eds.) PETRI NETS 2010. LNCS, vol. 6128, pp. 226–245. Springer, Heidelberg (2010)Google Scholar
  26. 26.
    Verbeek, E.: Decomposed process mining with DivideAndConquer. Proceedings of the BPM Demo Sessions 2014, 1–5 (2014)Google Scholar
  27. 27.
    J. De Weerdt, M. De Backer, J. Vanthienen, and B. Baesens. Leveraging Process Discovery With Trace Clustering and Text Mining for Intelligent Analysis of Incident Management Processes. In: IEEE Congress on Evolutionary Computation (CEC 2012), pp. 1–8 (2012)Google Scholar
  28. 28.
    Weijters, A.J.M.M., van der Aalst, W.M.P.: Rediscovering Workflow Models from Event-Based Data using Little Thumb. Integrated Computer-Aided Engineering 10(2), 151–162 (2003)Google Scholar
  29. 29.
    van der Werf, J.M.E.M., van Dongen, B.F., Hurkens, C.A.J., Serebrenik, A.: Process Discovery using Integer Linear Programming. Fundamenta Informaticae 94, 387–412 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Wil M. P. van der Aalst
    • 1
    • 2
    Email author
  • Anna Kalenkova
    • 2
  • Vladimir Rubin
    • 2
  • Eric Verbeek
    • 1
  1. 1.Eindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.National Research University Higher School of EconomicsMoscowRussia

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