Visual Support to Filtering Cases for Process Discovery

Working with average-sized event logs is still a major task in process mining, where the main goal is to gain process-related insights based on event logs created by a wide variety of systems. An event log contains a sequence of events for every case that was handled by the system. Several discovery algorithms have been proposed and work well in specific cases but fail to be generic strategies. Moreover, there is no evidence that the existing strategies can handle events with a large number of variants. For this reason, a generic approach is needed to allow experts to explore event log data and decompose information into a series of smaller problems, to identify outliers and relations between the analyzed cases. In this paper we present a visual filtering approach for event logs that makes process analysis tasks more feasible and tractable. To evaluate our approach, we have developed a visual filtering tool and used it with the event log from BPI