论文标题
一种深度学习方法，用于修复事件日志中的丢失活动标签以进行过程挖掘
A Deep Learning Approach for Repairing Missing Activity Labels in Event Logs for Process Mining
论文作者
论文摘要
流程挖掘是一个相对较新的主题，它在传统的过程建模和数据挖掘之间建立了桥梁。过程发现是过程挖掘最关键的部分之一，旨在从事件日志自动发现过程模型。当事件日志中缺少活动标签时，现有过程发现算法的性能会受到影响。已经提出了几种修复缺少活动标签的方法，但是当丢失大量活动标签时，它们的准确性可能会下降。在本文中，我们提出了一个基于LSTM的预测模型，以预测事件日志中缺少的活动标签。提出的模型同时将事件的前缀和后缀序列同时为输入。事件日志的其他属性也用于提高性能。我们对几个公开可用数据集的评估表明，在事件日志中修复缺失的活动标签方面，所提出的方法的执行始终如一。
Process mining is a relatively new subject that builds a bridge between traditional process modeling and data mining. Process discovery is one of the most critical parts of process mining, which aims at discovering process models automatically from event logs. The performance of existing process discovery algorithms can be affected when there are missing activity labels in event logs. Several methods have been proposed to repair missing activity labels, but their accuracy can drop when a large number of activity labels are missing. In this paper, we propose an LSTM-based prediction model to predict the missing activity labels in event logs. The proposed model takes both the prefix and suffix sequences of the events with missing activity labels as input. Additional attributes of event logs are also utilized to improve the performance. Our evaluation of several publicly available datasets shows that the proposed method performed consistently better than existing methods in terms of repairing missing activity labels in event logs.
