Abstract
Automated process discovery techniques aim at extracting models from information system logs in order to shed light into the business processes supported by these systems. Existing techniques in this space are effective when applied to relatively small or regular logs, but otherwise generate large and spaghetti-like models. In previous work, trace clustering has been applied in an attempt to reduce the size and complexity of automatically discovered process models. The idea is to split the log into clusters and to discover one model per cluster. The result is a collection of process models – each one representing a variant of the business process – as opposed to an all-encompassing model. Still, models produced in this way may exhibit unacceptably high complexity. In this setting, this paper presents a two-way divide-and-conquer process discovery technique, wherein the discovered process models are split on the one hand by variants and on the other hand hierarchically by means of subprocess extraction. The proposed technique allows users to set a desired bound for the complexity of the produced models. Experiments on real-life logs show that the technique produces collections of models that are up to 64% smaller than those extracted under the same complexity bounds by applying existing trace clustering techniques.
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
Bose, R.P.J.C.: Process Mining in the Large: Preprocessing, Discovery, and Diagnostics. PhD thesis, Eindhoven University of Technology, Eindhoven (2012)
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.) BPM 2009 Workshops. LNBIP, vol. 43, pp. 170–181. Springer, Heidelberg (2010)
Bose, R.P.J.C., Verbeek, E.H.M.W., van der Aalst, W.M.P.: Discovering hierarchical process models using prom. In: Nurcan, S. (ed.) CAiSE Forum 2011. LNBIP, vol. 107, pp. 33–48. Springer, Heidelberg (2012)
de Medeiros, A.K.A., Guzzo, A., Greco, G., van der Aalst, W.M.P., Weijters, A.J.M.M., van Dongen, B.F., Saccà, D.: Process mining based on clustering: A quest for precision. In: ter Hofstede, A.H.M., Benatallah, B., Paik, H.-Y. (eds.) BPM Workshops 2007. LNCS, vol. 4928, pp. 17–29. Springer, Heidelberg (2008)
Dijkman, R.M., Dumas, M., van Dongen, B.F., Käärik, R., Mendling, J.: Similarity of business process models: Metrics and evaluation. Inf. Syst. 36(2), 498–516 (2011)
Dumas, M., García-Bañuelos, L., La Rosa, M., Uba, R.: Fast detection of exact clones in business process model repositories. Inf. Syst. 38(4), 619–633 (2012)
Ekanayake, C.C., Dumas, M., García-Bañuelos, L., La Rosa, M., ter Hofstede, A.H.M.: Approximate clone detection in repositories of business process models. In: Barros, A., Gal, A., Kindler, E. (eds.) BPM 2012. LNCS, vol. 7481, pp. 302–318. Springer, Heidelberg (2012)
Greco, G., Guzzo, A., Pontieri, L.: Mining taxonomies of process models. Data Knowl. Eng. 67(1), 74–102 (2008)
Greco, G., Guzzo, A., Pontieri, L., Saccà, D.: Discovering expressive process models by clustering log traces. IEEE Trans. Knowl. Data Eng. 18(8), 1010–1027 (2006)
La Rosa, M., Reijers, H.A., van der Aalst, W.M.P., Dijkman, R.M., Mendling, J., Dumas, M., García-Bañuelos, L.: APROMORE: An Advanced Process Model Repository. Expert Syst. Appl. 38(6) (2011)
Mendling, J., Reijers, H.A., Cardoso, J.: What Makes Process Models Understandable? In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 48–63. Springer, Heidelberg (2007)
Mendling, J., Sánchez-González, L., García, F., La Rosa, M.: Thresholds for error probability measures of business process models. J. Syst. Software 85(5), 1188–1197 (2012)
Reijers, H.A., Mendling, J.: A study into the factors that influence the understandability of business process models. IEEE T. Syst. Man Cy. A 41(3), 449–462 (2011)
La Rosa, M., Dumas, M., Uba, R., Dijkman, R.: Business process model merging: An approach to business process consolidation. ACM T. Softw. Eng. Meth. 22(2) (2013)
Song, M., Günther, C.W., van der Aalst, W.M.P.: Improving process mining with trace clustering. J. Korean Inst. of Industrial Engineers 34(4), 460–469 (2008)
Song, M., Günther, C.W., van der Aalst, W.M.P.: Trace clustering in process mining. In: Ardagna, D., Mecella, M., Yang, J. (eds.) BPM 2008 Workshops. LNBIP, vol. 17, pp. 109–120. Springer, Heidelberg (2009)
van der Aalst, W.M.P.: Process Mining - Discovery, Conformance and Enhancement of Business Processes. Springer (2011)
van der Werf, J.M.E.M., van Dongen, B.F., Hurkens, C.A.J., Serebrenik, A.: Process discovery using integer linear programming. Fundam. Inform. 94(3-4), 387–412 (2009)
Vanhatalo, J., Völzer, H., Koehler, J.: The Refined Process Structure Tree. Data Knowl. Eng. 68(9), 793–818 (2009)
Veiga, G.M., Ferreira, D.R.: Understanding spaghetti models with sequence clustering for prom. In: Rinderle-Ma, S., Sadiq, S., Leymann, F. (eds.) BPM 2009. LNBIP, vol. 43, pp. 92–103. Springer, Heidelberg (2010)
De Weerdt, J., De Backer, M., Vanthienen, J., Baesens, B.: A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs. Inf. Syst. 37(7), 654–676 (2012)
Weijters, A.J.M.M., Ribeiro, J.T.S.: Flexible heuristics miner (fhm). In: CIDM, pp. 310–317. IEEE (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ekanayake, C.C., Dumas, M., García-Bañuelos, L., La Rosa, M. (2013). Slice, Mine and Dice: Complexity-Aware Automated Discovery of Business Process Models. In: Daniel, F., Wang, J., Weber, B. (eds) Business Process Management. Lecture Notes in Computer Science, vol 8094. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40176-3_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-40176-3_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40175-6
Online ISBN: 978-3-642-40176-3
eBook Packages: Computer ScienceComputer Science (R0)