Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
de Medeiros, A.: Genetic Process Mining. PhD thesis, Technische Universiteit Eindhoven (2006)
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)
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)
Rozinat, A., van der Aalst, W.M.P.: Conformance checking of processes based on monitoring real behavior. Information Systems 33(1), 64–95 (2008)
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)
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)
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)
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)
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)
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)
Weijters, A., van der Aalst, W.M.P., de Medeiros, A.: Process mining with the heuristics miner-algorithm. Technische Universiteit Eindhoven 166 (2006)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Vázquez-Barreiros, B., Mucientes, M., Lama, M. (2014). A Genetic Algorithm for Process Discovery Guided by Completeness, Precision and Simplicity. In: Sadiq, S., Soffer, P., Völzer, H. (eds) Business Process Management. BPM 2014. Lecture Notes in Computer Science, vol 8659. Springer, Cham. https://doi.org/10.1007/978-3-319-10172-9_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-10172-9_8
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10171-2
Online ISBN: 978-3-319-10172-9
eBook Packages: Computer ScienceComputer Science (R0)