DeepAlign: Alignment-based Process Anomaly Correction using Recurrent Neural Networks

TL;DR

DeepAlign leverages bidirectional recurrent neural networks and sequence alignment techniques to detect and correct process anomalies, significantly outperforming existing conformance checking methods on synthetic event logs.

Contribution

Introduces a novel alignment-based anomaly correction method using bidirectional RNNs, improving accuracy over state-of-the-art techniques.

Findings

Achieved an overall F1 score of 0.9572 on synthetic datasets.

Outperformed existing methods with a higher correction accuracy.

Demonstrated effectiveness on a diverse set of synthetic event logs.

Abstract

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 $0.6411$.