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BINet: Multi-perspective business process anomaly classification
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DOI：10.1016/j.is.2019.101458.png)
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Abstract
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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 at a case level but also at 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. (c) 2019 Elsevier Ltd. All rights reserved.
Keywords:
Business process management
Anomaly detection
Artificial process intelligence
Deep learning
Recurrent neural networks
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