基于直追关系的混沌过滤法预处理事件日志

Tengzi Lv, Xiugang Gong, Na Gong, Kaiyu Li
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摘要

流程发现旨在从事件日志中发现流程模型,以描述实际业务流程。事件日志的质量会影响流程模型的质量,因此可以使用预处理方法来提高事件日志的质量。在实际业务场景中可能存在混乱活动,混乱活动的发生与流程中的其他活动无关,可以以任何频率发生在事件日志中的任何位置。因此,混乱活动会严重影响流程发现的模型质量。过滤事件日志中的混沌活动可以有效提高事件日志的质量,从而提高流程模型的质量。传统的混沌活动过滤算法很难兼顾准确性和时间性能。因此,本文提出了一种直接过滤混沌活动的方法。通过分析活动之间的关系,根据混沌活动的特征,以活动的直接跟随关系为判断条件,识别日志中的混沌活动,实现对事件日志中混沌活动的过滤。此外,本文还提出了一种间接混沌活动过滤方法,通过分析不同活动的存在对日志整体混沌度的影响,识别并过滤日志中的混沌活动。在多个仿真/真实数据集上比较了所提出的方法和传统的混沌活动过滤方法,分析了混沌活动过滤前后生成的多组事件日志和流程模型之间的准确性和运行时间,进一步验证了所提出方法的有效性和可行性。通过总结实验结果,发现所提出的混沌活动过滤方法的准确性高于基于频率的过滤方法,接近于基于熵的混沌活动过滤方法。此外,与实验中使用的其他过滤方法相比,本文提出的混沌活动过滤方法对模拟日志的过滤效率平均提高了 23.4%,对真实事件日志的过滤效率平均提高了 84.25%。结论是,与其他过滤方法相比,本文提出的混沌活动过滤方法具有更高的准确性,能有效提高混沌活动过滤的时间性能。因此,本文提出的混沌活动过滤方法可以兼顾准确性和时间性能,并能在一定程度上保证过滤后事件日志的完整性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pre-Processing Event Logs by Chaotic Filtering Approaches Based on the Direct Following Relationship
Process discovery aims to discover process models from event logs to describe actual business processes. The quality of event logs has an impact on the quality of process models, so preprocessing methods can be used to improve the quality of event logs. 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 model 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 makes it difficult to balance accuracy and time performance. Therefore, a direct method for filtering chaotic activities is proposed in this paper. By analyzing the relationship between activities, chaotic activities are identified in the log according to the characteristics of chaotic activities and the direct following relationship of activities as the judgment condition, and the filtering of chaotic activities in the event log is realized. In addition, this paper proposes an indirect chaotic activity filtering method, which identifies and filters chaotic activities in the log by analyzing the influence of the existence of different activities on the overall chaos degree of the log. The proposed method is compared with the traditional chaotic activity filtering method 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 method. By summarizing the experimental results, it is found that the accuracy of the proposed chaotic activity filtering methods is greater than that of the frequency-based filtering method and is close to that of the entropy-based chaotic activity filtering methods. Moreover, compared with other filtering methods used in the experiment, the chaotic activity filtering method proposed in this paper can improve the efficiency by 23.4% on average for simulation logs, and by 84.25% on average for real event logs. It is concluded that compared with other filtering methods, the proposed chaotic activity filtering methods have higher accuracy and can effectively improve the time performance of chaotic activity filtering. Therefore, the chaotic activity filtering method proposed in this paper can balance the accuracy and time performance, and can ensure the integrity of the filtered event log to a certain extent.
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