A Genetic Algorithm for Process Discovery Guided by Completeness, Precision and Simplicity

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8659)


Several process discovery algorithms have been presented in the last years. These approaches look for complete, precise and simple models. Nevertheless, none of the current proposals obtains a good integration between the three objectives and, therefore, the mined models have differences with the real models. In this paper we present a genetic algorithm (ProDiGen) with a hierarchical fitness function that takes into account completeness, precision and simplicity. Moreover, ProDiGen uses crossover and mutation operators that focus the search on those parts of the model that generate errors during the processing of the log. The proposal has been validated with 21 different logs. Furthermore, we have compared our approach with two of the state of the art algorithms.


Process mining process discovery Petri nets genetic mining 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Buijs, J., van Dongen, B., van der Aalst, W.M.P.: Quality dimensions in process discovery: The importance of fitness, precision, generalization and simplicity. International Journal of Cooperative Information Systems 23(01) (2014)Google Scholar
  2. 2.
    de Medeiros, A.: Genetic Process Mining. PhD thesis, Technische Universiteit Eindhoven (2006)Google Scholar
  3. 3.
    Dumas, M., ter Hofstede, A., van der Aalst, W.M.P.: Process-aware information systems: bridging people and software through process technology. Wiley-Interscience (2005)Google Scholar
  4. 4.
    Günther, C.W., van der Aalst, W.M.P.: Fuzzy mining – adaptive process simplification based on multi-perspective metrics. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 328–343. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Rozinat, A., van der Aalst, W.M.P.: Conformance checking of processes based on monitoring real behavior. Information Systems 33(1), 64–95 (2008)CrossRefGoogle Scholar
  6. 6.
    Sánchez-González, L., García, F., Mendling, J., Ruiz, F., Piattini, M.: Prediction of business process model quality based on structural metrics. In: Parsons, J., Saeki, M., Shoval, P., Woo, C., Wand, Y. (eds.) ER 2010. LNCS, vol. 6412, pp. 458–463. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    van der Aalst, W.M.P., Adriansyah, A., van Dongen, B.: Replaying history on process models for conformance checking and performance analysis. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2(2), 182–192 (2012)Google Scholar
  8. 8.
    van der Aalst, W.M.P., Ter Hofstede, A.H., Kiepuszewski, B., Barros, A.P.: Workflow patterns. Distributed and Parallel Databases 14(1), 5–51 (2003)CrossRefGoogle Scholar
  9. 9.
    van der Aalst, W.M.P., Weijters, A., Maruster, L.: Workflow mining: Discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering 16(9), 1128–1142 (2004)CrossRefGoogle Scholar
  10. 10.
    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
  11. 11.
    vanden Broucke, S., Weerdt, J.D., Vanthienen, J., Baesens, B.: A comprehensive benchmarking framework (CoBeFra) for conformance analysis between procedural process models and event logs in ProM. In: 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 254–261. IEEE (2013)Google Scholar
  12. 12.
    Weijters, A., van der Aalst, W.M.P., de Medeiros, A.: Process mining with the heuristics miner-algorithm. Technische Universiteit Eindhoven 166 (2006)Google Scholar
  13. 13.
    Wen, L., Wang, J., Sun, J.: Mining invisible tasks from event logs. In: Dong, G., Lin, X., Wang, W., Yang, Y., Yu, J.X. (eds.) APWeb/WAIM 2007. LNCS, vol. 4505, pp. 358–365. Springer, Heidelberg (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Centro de Investigación en Tecnoloxías da Información (CiTIUS)University of Santiago de CompostelaSpain

Personalised recommendations