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

Process mining is a relatively new subject which builds a bridge between traditional process modelling 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 a 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 utilised to improve the performance. Our evaluation on several publicly available datasets show that the proposed method performed consistently better than existing methods to repair missing activity labels in event logs.