基于活动恢复集的有效低频行为分析方法

Effective infrequent behaviors analysis method based on activity recovery sets

低频行为识别是揭示业务流程重要信息和优化流程模型的方法之一,现有流程发现方法忽略了数据影响链对低频行为产生的影响,导致了一些低频行为被视为噪声直接过滤掉。针对这一问题,提出了一种基于活动恢复集的有效低频行为分析方法。首先根据事件日志中的行为重要性过滤日志,并构建初始流程模型;其次从事务日志中提取活动的输入输出数据项,并根据这些数据项构造活动影响链图,在此基础上获取每个活动基于迹的活动恢复集;最后根据活动恢复集来计算每条迹的行为容忍度以区分有效低频行为和噪声。实验结果表明,与其他方法相比,该方法能够有效区分有效低频行为与噪声,并且从拟合度、精度以及简单性方面提高了流程模型的质量。该方法考虑了由活动恢复集而导致的偏差情况,可以成功识别事件日志中的有效低频行为,从而优化了流程模型。

Infrequent behavior recognition is one of the methods to reveal important information about business processes and optimize process models.Existing process discovery methods have overlooked the impact of data influence chains on infrequent behavior,resulting in some infrequent behavior being considered as noise and filtered out directly.To address this issue,this paper proposed a novel infrequent behavior analysis method based on activity recovery sets.Firstly,it filtered the event logs based on the importance of behavior and constructed an initial process model.Secondly,it extracted input and output data items of activities from transaction logs,and constructed an activity influence chain graph based on these data items.It obtained activity recovery sets based on these graphs.Finally,it calculated the behavior tolerance of each trace using the activity recovery sets to distinguish effective infrequent behavior from noise.The experimental results indicate that,compared to other methods,this study effectively distinguishes valid infrequent behaviors from noise and improves the quality of the process model in terms of fitness,precision,and simplicity.This method considers the biases caused by the activity recovery set and successfully identifies valid infrequent behaviors in event logs,thereby optimizing the process model.