DeepAlign: Alignment-based Process Anomaly Correction using Recurrent Neural Networks
This addresses process anomaly correction for business process management, offering a significant improvement over existing methods.
The paper tackles process anomaly correction by proposing DeepAlign, a method using recurrent neural networks and bidirectional beam search to detect and correct anomalies in event logs, achieving an F1 score of 0.9572 compared to 0.6411 for the best state-of-the-art method.
In this paper, we propose DeepAlign, a novel approach to multi-perspective process anomaly correction, based on recurrent neural networks and bidirectional beam search. At the core of the DeepAlign algorithm are two recurrent neural networks trained to predict the next event. One is reading sequences of process executions from left to right, while the other is reading the sequences from right to left. By combining the predictive capabilities of both neural networks, we show that it is possible to calculate sequence alignments, which are used to detect and correct anomalies. DeepAlign utilizes the case-level and event-level attributes to closely model the decisions within a process. We evaluate the performance of our approach on an elaborate data corpus of 252 realistic synthetic event logs and compare it to three state-of-the-art conformance checking methods. DeepAlign produces better corrections than the rest of the field reaching an overall $F_1$ score of $0.9572$ across all datasets, whereas the best comparable state-of-the-art method reaches $0.6411$.