An Event Log Repair Method Based on Masked Transformer Model

Abstract

The effectiveness of business process analysis heavily relies on the quality of event logs. However, the presence of outliers and missing values often compromises the integrity of event logs, consequently exerting adverse effects on process analysis and associated decision-making. Existing log repair research mainly focuses on the reconstruction of missing activity, whereas few efforts are carried out from the perspective of predicting missing activity. This paper introduces a log repair approach based on a masked Transformer, which innovatively combines the self-attention mechanism of Transformers with the task of event log repair. Firstly, by employing various masking strategies, we simulate diverse low-quality event log scenarios that may occur in practical situations. Subsequently, a Masked Language Model is trained on preprocessed datasets to predict masked activities by leveraging contextual information within traces, thereby capturing behavioral information of activities in the event log. Upon completion of model training, we apply it to real event log data for repair tasks. The proposed approach, originating from the perspective of event logs, does not rely on any a priori knowledge related to business process models for generating event logs. Experimental results demonstrate that the masked Transformer-based approach outperforms baseline methods in most event log repair tasks.