A Deep Learning Approach for Repairing Missing Activity Labels in Event Logs for Process Mining

• Published in: Information (Basel) Vol. 13; no. 5; p. 234
• Main Authors: Lu, Yang, Chen, Qifan, Poon, Simon K.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2022
• Subjects: Airports; Algorithms; business process management; data management; Data mining; data quality; Datasets; Deep learning; incomplete event logs; Labels; Machine learning; Neural networks; Performance enhancement; Performance evaluation; Prediction models; process mining
• ISSN: 2078-2489; 2078-2489
• DOI: 10.3390/info13050234

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. Like other data mining techniques, the performance of existing process discovery algorithms can be affected when there are missing activity labels in event logs. In this paper, we assume that the control-flow information in event logs could be useful in repairing missing activity labels. We propose an LSTM-based prediction model, which takes both the prefix and suffix sequences of the events with missing activity labels as input to predict missing activity labels. 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.