Mining Process Performance from Event Logs

Abstract

In systems where process executions are not strictly enforced by a predefined process model, obtaining reliable performance information is not trivial. 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 exploited the alignment technique [2] 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 insights into frequently occurring deviations and how such insights can be exploited to repair the original process models to better reflect reality. Furthermore, we showed that the alignments can be further exploited to obtain performance information. All analysis in this paper is performed using plug-ins within the open-source process mining toolkit ProM.