Abstract
Process mining is a discipline that uses techniques to extract knowledge from event logs recorded by information systems in most companies these days. Among main perspectives of process mining, organizational and time perspectives focus on information about resources stored on the event logs and timing and frequency of the events, respectively. In this paper we introduce a method that combines organizational and time perspectives of process mining with a decision support tool called decision trees. The method takes the information of historical process data by means of an event log, generates a decision tree, annotates the decision tree with processing times, and recommends the best performer for a given running instance of the process. We finally illustrate the method through several experiments using a developed plug-in for the process mining framework ProM, first using synthetic data and then using a real-life event log.
The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-02922-1_10
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References
van der Aalst, W.M.P.: Process mining: Discovery, conformance and enhancement of business processes. Springer, Heidelberg (2011)
Magee, J.F.: Decision trees for decision making. Harvard Bus. Rev. 42(4), 126–138 (1964)
Kim, A., Jung, J.-Y.: A process mining technique for performer recommendation using decision tree. In: Korean Institute of Industrial Engineers Conference (2012)
De Medeiros, A.K.A., Weijters, A.J.M.M.: ProM Framework Tutorial. TechnischeUniversiteit Eindhoven, The Netherlands (2009)
van der Aalst, W.M.P., Reijers, H.A., Weijters, A.J.M.M., van Dongen, B.F., Alves de Medeiros, A.K., Song, M., Verbeek, H.M.W.: Business process mining: an industrial application. Inform. Syst. 32(5), 713–732 (2007)
Liu, Y., Wang, J., Yang, Y., Sun, J.: A semi-automatic approach for workflow staff assignment. Comput. Ind. 59(5), 463–476 (2008)
Ly, L.T., Rinderle, S., Dadam, P., Reichert, M.: Mining staff assignment rules from event-based data. In: Bussler, C.J., Haller, A. (eds.) BPM 2005. LNCS, vol. 3812, pp. 177–190. Springer, Heidelberg (2006)
Rozinat, A., van der Aalst, W.M.P.: Decision mining in ProM. In: Dustdar, S., Fiadeiro, J.L., Sheth, A.P. (eds.) BPM 2006. LNCS, vol. 4102, pp. 420–425. Springer, Heidelberg (2006)
Quinlan, J.R.: Decision trees and decision-making. IEEE T. Syst. Man Cyb. 20(2), 339–346 (1990)
van Dongen, B.F., de Medeiros, A.K.A., Verbeek, H.M.W(E.), Weijters, A.J.M.M.T., van der Aalst, W.M.P.: The ProM framework: Anew era in process mining tool support. In: Ciardo, G., Darondeau, P. (eds.) ICATPN 2005. LNCS, vol. 3536, pp. 444–454. Springer, Heidelberg (2005)
Verbeek, H.M.W., Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: ProM 6: The process mining toolkit. In: BPM Demonstration Track, vol. 615, pp. 34–39 (2010)
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Obregon, J., Kim, A., Jung, JY. (2013). DTMiner: A Tool for Decision Making Based on Historical Process Data. In: Song, M., Wynn, M.T., Liu, J. (eds) Asia Pacific Business Process Management. AP-BPM 2013. Lecture Notes in Business Information Processing, vol 159. Springer, Cham. https://doi.org/10.1007/978-3-319-02922-1_6
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DOI: https://doi.org/10.1007/978-3-319-02922-1_6
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