Log-Based Simplification of Process Models

  • Javier De San Pedro
  • Josep Carmona
  • Jordi Cortadella
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9253)


The visualization of models is essential for user-friendly human-machine interactions during Process Mining. A simple graphical representation contributes to give intuitive information about the behavior of a system. However, complex systems cannot always be represented with succinct models that can be easily visualized. Quality-preserving model simplifications can be of paramount importance to alleviate the complexity of finding useful and attractive visualizations.

This paper presents a collection of log-based techniques to simplify process models. The techniques trade off visual-friendly properties with quality metrics related to logs, such as fitness and precision, to avoid degrading the resulting model. The algorithms, either cast as optimization problems or heuristically guided, find simplified versions of the initial process model, and can be applied in the final stage of the process mining life-cycle, between the discovery of a process model and the deployment to the final user. A tool has been developed and tested on large logs, producing simplified process models that are one order of magnitude smaller while keeping fitness and precision under reasonable margins.


State Machine Utilization Score Reduction Rule Integer Linear Program Model Marked Graph 
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 (2011)Google Scholar
  2. 2.
    Fahland, D., van der Aalst, W.M.P.: Simplifying discovered process models in a controlled manner. Information Systems 38(4), 585–605 (2013)CrossRefGoogle Scholar
  3. 3.
    Gansner, E.R., Koutsofios, E., North, S.C., Vo, K.: A technique for drawing directed graphs. IEEE Trans. Software Eng. 19(3), 214–230 (1993)CrossRefGoogle Scholar
  4. 4.
    van der Werf, J.M.E.M., van Dongen, B.F., Hurkens, C.A.J., Serebrenik, A.: Process discovery using integer linear programming. In: van Hee, K.M., Valk, R. (eds.) PETRI NETS 2008. LNCS, vol. 5062, pp. 368–387. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  5. 5.
    Murata, T.: Petri nets: Properties, analysis and applications. Proceedings of the IEEE 77(4), 541–574 (1989)CrossRefGoogle Scholar
  6. 6.
    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) CrossRefGoogle Scholar
  7. 7.
    Adriansyah, A.: Aligning observed and modeled behavior. PhD thesis, Technische Universiteit Eindhoven (2014)Google Scholar
  8. 8.
    Valdes, J., Tarjan, R.E., Lawler, E.L.: The recognition of series parallel digraphs. SIAM J. Comput. 11(2), 298–313 (1982)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Desel, J., Esparza, J.: Free choice Petri nets 40 (1995)Google Scholar
  10. 10.
    Gurobi Optimization: Gurobi Optimizer reference manual (2015)Google Scholar
  11. 11.
    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, Heidelberg (2014) Google Scholar
  12. 12.
    McMillan, K.L.: Using unfoldings to avoid the state explosion problem in the verification of asynchronous circuits. In: von Bochmann, G., Probst, D.K. (eds.) CAV 1992. LNCS, vol. 663, pp. 164–177. Springer, Heidelberg (1993)Google Scholar
  13. 13.
    van der Aalst, W.M.P., de Medeiros, A.K.A., Weijters, A.J.M.M.T.: Genetic process mining. In: Ciardo, G., Darondeau, P. (eds.) ICATPN 2005. LNCS, vol. 3536, pp. 48–69. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  14. 14.
    Buijs, J.: Flexible Evolutionary Algorithms for Mining Structured Process Models. PhD thesis, Technische Universiteit Eindhoven (2014)Google Scholar
  15. 15.
    Vázquez-Barreiros, B., Mucientes, M., Lama, M.: ProDiGen: Mining complete, precise and minimal structure process models with a genetic algorithm. Information Sciences 294, 315–333 (2015)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Günther, C.: Process Mining in Flexible Environments. PhD thesis, Technische Universiteit Eindhoven (2009)Google Scholar
  17. 17.
    Weijters, A.J.M.M., Ribeiro, J.T.S.: Flexible heuristics miner (FHM). In: Computational Intelligence and Data Mining (CIDM), pp. 310–317 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Javier De San Pedro
    • 1
  • Josep Carmona
    • 1
  • Jordi Cortadella
    • 1
  1. 1.Universitat Politècnica de CatalunyaBarcelonaSpain

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