Abstract: In practical business processes,low-quality event logs due to outliers and missing values are often unavoidable.Low-quality event logs can degrade the performance of associated algorithms for process mining,which in turn interferes with the correct implementation of decisions.Under the condition that the system reference model is unknown,when performing log anomaly detection and repair work,the existing methods have the problems of needing to manually set thresholds,do not understand what behavior constraints the prediction model learns,and poor interpretability of repair results.Inspired by the fact that the pre-trained language model BERT using the masking strategy can self-supervise learning of general semantics in text through context information,combined with attention mechanism with multi-layer and multi-head,this paper proposes the model BERT4Log and weak behavioral profiles theory to perform an interpretable repair process for low-quality event logs.The proposed repair method does not need to set a threshold in advance,and only needs to perform self-supervised training once.At the same time,the method uses the weak behavioral profiles theory to quantify the degree of behavioral repair of logs.And combined with the multi-layer multi-head attention mechanism to realize the detailed interpretation process about the specific prediction results.Finally,the performance of the proposed method is evaluated on a set of public datasets,and compared with the current research with the best performance.Experimental results show that the repair performance of BERT4Log is better than the comparative research,and at the same time,the model can learn weak behavioral profiles and achieve detailed interpretation of repair results.

Key words: Event log repair, Weak behavioral profiles, BERT, Interpretable model, Attention mechanism