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Title |
Chaotic Activity Filtering Approach of Business Process Based on The Directly-Follows Relation |
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Author |
Tengzi Lv, Xiugang Gong, Muhammad Tahir, Na Gong, Kaiyu Li |
| Citation |
Vol. 25 No. 2 pp. 169-187 |
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Abstract |
Process discovery aims to discover process models from event logs to describe actual business processes. However, chaotic activities may exist in real business scenarios, and the occurrence of chaotic activities is independent of other activities in the process and can occur at any location in the event log at any frequency. Therefore, chaotic activities seriously affect the approach quality of process discovery. Filtering chaotic activities in event logs can effectively improve the quality of event logs and thus improve the quality of process models. The traditional chaotic activity filtering algorithm is difficult to balance accuracy and time performance. Therefore, new approaches for filtering chaotic activities are proposed in this paper. By analyzing the relation between activities, chaotic activities are identified in the log according to the characteristics of chaotic activities and the directly-follows relation of activities as the judgment conditions, and the filtering of chaotic activities in the event log is realized. The proposed approaches are compared with the traditional chaotic activity filtering approaches on several simulation/real data sets, and the accuracy and running time between the multi-group event logs and the process models generated before and after chaotic activity filtering are analyzed, further verifying the effectiveness and feasibility of the proposed approaches. |
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Keywords |
process mining; chaotic activities; process model; directly-follows relation; event log. |
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