LogRank: An Approach to Sample Business Process Event Log for Efficient Discovery

摘要

Considerable amounts of business process event logs can be collected by modern information systems. Process discovery aims to uncover a process model from an event log. Many process discovery approaches have been proposed, however, most of them have difficulties in handling large-scale event logs. Motivated by PageRank, in this paper we propose LogRank, a graph-based ranking model, for event log sampling. Using LogRank, a large-scale event log can be sampled to a smaller size that can be efficiently handled by existing discovery approaches. Moreover, we introduce an approach to measure the quality of a sample log with respect to the original one from a discovery perspective. The proposed sampling approach has been implemented in the open-source process mining toolkit ProM. The experimental analyses with both synthetic and real-life event logs demonstrate that the proposed sampling approach provides an effective solution to improve process discovery efficiency as well as ensuring high quality of the discovered model.