摘要
在流程挖掘领域,众多流程操作高度依赖于事件日志中精确的时间戳信息.因此,与时间戳相关的质量问题影响尤为显著,特别是相同时间戳错误,这种错误会引发误导性的流程见解,进而造成严重的流程偏差.现有研究在处理此类错误时,缺乏对事件间长期依赖关系以及属性间潜在关联性的充分考量,在一定程度上限制了相同时间戳错误的修复精度.针对这一问题,本文提出了一种基于分层Transformer模型修复相同时间戳错误的方法.该方法通过分层信息传递结合多视角交互,捕获事件间的长距离行为依赖以及属性间的深层关联信息,逐层完成对错误事件重排序以及对应时间戳的预测任务,继而实现对相同时间戳错误事件日志的有效修复.通过4个公开可用的数据集进行评估,结果表明,所提方法能够有效提高相同时间戳错误的修复精度.
In the field of process mining,numerous process operations are highly dependent on accurate timestamp information in event logs.Consequently,quality issues related to timestamps have particularly significant impacts,especially identical timestamp errors,which can lead to misleading process insights and subsequently result in severe process deviations.Existing research addressing such errors lacks sufficient consideration of long-term dependencies between events and potential correlations between attributes,limiting the accuracy of repairing identical timestamp errors.To address this issue,a method for repairing identical timestamp errors based on a hierarchical Transformer model is proposed.This method captures long-distance behavioral dependencies between events and deep correlation information between attributes through hierarchical information propagation combined with multi-view interaction.The tasks of reordering erroneous events and predicting corresponding timestamps are accomplished layer by layer,thus effectively repairing event logs with identical timestamp errors.Evaluations conducted on four publicly available datasets demonstrate that the proposed method can effectively improve the accuracy of repairing identical timestamp errors.
作者
徐猛
谢凯
XU Meng;XIE Kai(College of Mathematics&Big Data,Anhui University of Science&Technology,Huainan 232001,China;Anhui Province Engineering Laboratory for Big Data Analysis and Early Warning Technology of Coal Mine Safety,Anhui University of Science&Technology,Huainan 232001,China)
出处
《计算机系统应用》
2025年第8期217-227,共11页
Computer Systems & Applications
基金
安徽省重点研究与开发计划(2022a05020005)。
