BINet: Multi-perspective Business Process Anomaly Classification

Timo Nolle, Stefan Luettgen, Alexander Seeliger, Max Mühlhäuser
2/8/2019
cs.AI

Official code: tnolle/binet (29 stars, GPL-3.0, last commit 2021-04-03).

Abstract

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.

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Code Implementations(1)

tnolle/binetOfficial100%
GPL-3.0

This is the complementary code repository for the BINet papers.

296Python, Jupyter NotebookMar 7, 20185 years agoGPL-3.0

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Cite this paper

@article{nolle2019binet,
  title  = {BINet: Multi-perspective Business Process Anomaly Classification},
  author = {Timo Nolle and Stefan Luettgen and Alexander Seeliger and Max Mühlhäuser},
  year   = {2019},
  eprint = {1902.03155},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1902.03155}
}

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