Abstract
Process-aware Information Systems typically log events (e.g., in transaction logs or audit trails) related to the actual business process executions. Proper analysis of these execution logs can yield important knowledge that can help organizations to improve the quality of their services. Starting from a process model, which can be discovered by conventional process mining algorithms, we analyze how data attributes influence the choices made in the process based on past process executions. Decision mining, also referred to as decision point analysis, aims at the detection of data dependencies that affect the routing of a case. In this paper we describe how machine learning techniques can be leveraged for this purpose, and we present a Decision Miner implemented within the ProM framework.
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References
van der Aalst, W.M.P.: Business Alignment: Using Process Mining as a Tool for Delta Analysis. In: Grundspenkis, J., Kirikova, M. (eds.) Proceedings of the 5th Workshop on Business Process Modeling, Development and Support (BPMDS 2004), Caise 2004 Workshops, vol. 2, pp. 138–145. Riga Technical University, Latvia (2004)
van der Aalst, W.M.P., van Dongen, B.F., Herbst, J., Maruster, L., Schimm, G., Weijters, A.J.M.M.: Workflow Mining: A Survey of Issues and Approaches. Data and Knowledge Engineering 47(2), 237–267 (2003)
van der Aalst, W.M.P., Weijters, A.J.M.M., Maruster, L.: Workflow Mining: Discovering Process Models from Event Logs. IEEE Transactions on Knowledge and Data Engineering 16(9), 1128–1142 (2004)
Adam, O., Thomas, O., Loos, P.: Soft Business Process Intelligence — Verbesserung von Geschäftsprozessen mit Neuro-Fuzzy-Methoden. In: Lehner, F., et al. (eds.) Multikonferenz Wirtschaftsinformatik 2006, pp. 57–69. GITO-Verlag, Berlin (2006)
Grigori, D., Casati, F., Castellanos, M., Dayal, U., Sayal, M., Shan, M.-C.: Business Process Intelligence. Computers in Industry 53(3), 321–343 (2004)
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)
Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Rozinat, A., van der Aalst, W.M.P.: Conformance Testing: Measuring the Fit and Appropriateness of Event Logs and Process Models. In: Bussler, C.J., Haller, A. (eds.) BPM 2005. LNCS, vol. 3812, pp. 163–176. Springer, Heidelberg (2006)
Rozinat, A., van der Aalst, W.M.P.: Decision Mining in Business Processes. BPM Center Report BPM-06-10, BPMcenter.org. (2006)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
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Rozinat, A., van der Aalst, W.M.P. (2006). Decision Mining in ProM. In: Dustdar, S., Fiadeiro, J.L., Sheth, A.P. (eds) Business Process Management. BPM 2006. Lecture Notes in Computer Science, vol 4102. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11841760_33
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DOI: https://doi.org/10.1007/11841760_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38901-9
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