Mining Process Performance from Event Logs

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.