标题：LogRank: An approach to sample business process event log for efficient discovery
作者：Liu, Cong ;Pei, Yulong ;Zeng, Qingtian ;Duan, Hua
作者机构：[Liu, Cong ;Zeng, Qingtian ;Duan, Hua ] Shandong University of Science and Technology, Qingdao, China;[Pei, Yulong ] Eindhoven University of Technolog 更多
会议名称：11th International Conference on Knowledge Science, Engineering and Management, KSEM 2018
会议日期：August 17, 2018 - August 19, 2018
来源：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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.
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