标题:LogRank: An approach to sample business process event log for efficient discovery
作者:Liu, Cong ;Pei, Yulong ;Zeng, Qingtian ;Duan, Hua
通讯作者:Zeng, Qingtian
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)
出版年:2018
卷:11061 LNAI
页码:415-425
DOI:10.1007/978-3-319-99365-2_36
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.
© 2018, Springer Nature Switzerland AG.
收录类别:EI
资源类型:会议论文
TOP