Mining Activity Clusters from Low-Level Event Logs

Abstract. Process mining techniques have proven to be a valuable tool for ana-lyzing the execution of business processes. They rely on logs that identify events at an activity level, i.e., most process mining techniques assume that the infor-mation system explicitly supports the notion of activities/tasks. This is often not the case and only low-level events are being supported and logged. For example, users may provide different pieces of data which together constitute a single ac-tivity. The technique introduced in this paper uses clustering algorithms to derive activity logs from lower-level data modification logs, as produced by virtually every information system. This approach was implemented in the context of the ProM framework and its goal is to widen the scope of processes that can be ana-lyzed using existing process mining techniques