BINet: Multi-perspective Business Process Anomaly Classification
This addresses anomaly detection for business process management, but it appears incremental as it builds on existing methods with specific improvements.
The paper tackles the problem of real-time multi-perspective anomaly detection in business process event logs by introducing BINet, a neural network architecture that handles control flow and data perspectives, and it outperforms eight other state-of-the-art methods on synthetic and real-life datasets.
In this paper, we introduce BINet, a neural network architecture for real-time multi-perspective anomaly detection in business process event logs. BINet is designed to handle both the control flow and the data perspective of a business process. Additionally, we propose a set of heuristics for setting the threshold of an anomaly detection algorithm automatically. We demonstrate that BINet can be used to detect anomalies in event logs not only on a case level but also on event attribute level. Finally, we demonstrate that a simple set of rules can be used to utilize the output of BINet for anomaly classification. We compare BINet to eight other state-of-the-art anomaly detection algorithms and evaluate their performance on an elaborate data corpus of 29 synthetic and 15 real-life event logs. BINet outperforms all other methods both on the synthetic as well as on the real-life datasets.