Mining process performance from event logs : the BPI Challenge 2012 case study

A. Adriansyah, J.C.A.M. Buijs

Research output: Book/ReportReportAcademic

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Abstract

Having reliable performance information is often crucial in many business process improvement efforts. In systems where process executions are not strictly enforced by a predefined process model, obtaining such information is difficult. In this paper, we analyzed an event log of a real-life process, taken from a Dutch financial institute, using process mining techniques. In particular, we used the alignment technique to gain insights into the control flow and performance of the process execution. We showed that alignments between event logs and discovered process models from process discovery algorithms reveal frequent occurring deviations. Insights into these deviations can be exploited to repair the original process models to better reflect reality. Furthermore, we show that the projection of alignments onto process model provides reliable performance information. All analysis in this paper is performed using existing and dedicated plug-ins within the open-source process mining toolkit ProM.
Original languageEnglish
PublisherBPMcenter. org
Number of pages46
Publication statusPublished - 2012

Publication series

NameBPM reports
Volume1215

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Cite this

Adriansyah, A., & Buijs, J. C. A. M. (2012). Mining process performance from event logs : the BPI Challenge 2012 case study. (BPM reports; Vol. 1215). BPMcenter. org.
Adriansyah, A. ; Buijs, J.C.A.M. / Mining process performance from event logs : the BPI Challenge 2012 case study. BPMcenter. org, 2012. 46 p. (BPM reports).
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Adriansyah, A & Buijs, JCAM 2012, Mining process performance from event logs : the BPI Challenge 2012 case study. BPM reports, vol. 1215, BPMcenter. org.

Mining process performance from event logs : the BPI Challenge 2012 case study. / Adriansyah, A.; Buijs, J.C.A.M.

BPMcenter. org, 2012. 46 p. (BPM reports; Vol. 1215).

Research output: Book/ReportReportAcademic

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N2 - Having reliable performance information is often crucial in many business process improvement efforts. In systems where process executions are not strictly enforced by a predefined process model, obtaining such information is difficult. In this paper, we analyzed an event log of a real-life process, taken from a Dutch financial institute, using process mining techniques. In particular, we used the alignment technique to gain insights into the control flow and performance of the process execution. We showed that alignments between event logs and discovered process models from process discovery algorithms reveal frequent occurring deviations. Insights into these deviations can be exploited to repair the original process models to better reflect reality. Furthermore, we show that the projection of alignments onto process model provides reliable performance information. All analysis in this paper is performed using existing and dedicated plug-ins within the open-source process mining toolkit ProM.

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Adriansyah A, Buijs JCAM. Mining process performance from event logs : the BPI Challenge 2012 case study. BPMcenter. org, 2012. 46 p. (BPM reports).