Novel Approach to Discover Precise Process Model by Filtering out Log Chaotic Activities

Guo Yi ； Zhang Peng

《電腦學刊》 30卷4期 (2019/08) Pp. 140-150

https://doi.org/10.3966/199115992019083004013

摘要

Process mining technology automatically discovers business processes from the execution data of business processes. The real life business process event data logs usually contain chaotic activities which make the traditional event data log filtering approach not able to effectively filter the chaotic activities in event data logs. This paper proposes a novel chaotic activities filtering approach based on bidirectional causal dependence. The approach achieves the filtering of chaotic activities in event data logs by analyzing the bidirectional causal dependence between the model and event data logs and taking the precision as a constraint. At the end of this paper, the proposed approach is used in the Tianyuan big data platform to verify the effectiveness. By comparison experiments of chaotic activity filtering approach based on information entropy is evaluated from the aspects of time complexity. The evaluation shows that the approach can discover more precise process models through the analysis of precision between multiple sets of event data logs and the process models generated before and after chaotic activities filtering.

關鍵字

bidirectional causal dependence ； chaotic activities filtering ； precision ； process mining