# BINet: Multi-perspective Business Process Anomaly Classification

arXiv: https://arxiv.org/abs/1902.03155 · HTML: https://mlsanity.com/paper/1902.03155/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2019.

## Built on by (most cited first, 13 of 13)

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- [Trace Encoding in Process Mining: a survey and benchmarking](https://mlsanity.com/paper/2301.02167/) (2023)
- [Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction](https://mlsanity.com/paper/2502.10211/) (2025)
- [Machine learning in business process management: A systematic literature review](https://mlsanity.com/paper/2405.16396/) (2024)
- [DABL: Detecting Semantic Anomalies in Business Processes Using Large Language Models](https://mlsanity.com/paper/2406.15781/) (2024)
- [The Analysis of Online Event Streams: Predicting the Next Activity for Anomaly Detection](https://mlsanity.com/paper/2203.09619/) (2022)
- [Large Process Models: A Vision for Business Process Management in the Age of Generative AI](https://mlsanity.com/paper/2309.00900/) (2023)
- [Online anomaly detection using statistical leverage for streaming business process events](https://mlsanity.com/paper/2103.00831/) (2021)
- [Detecting Context-Aware Deviations in Process Executions](https://mlsanity.com/paper/2206.05532/) (2022)
- [Neuro-Symbolic Process Anomaly Detection](https://mlsanity.com/paper/2603.26461/) (2026)
- [Anomaly Correction of Business Processes Using Transformer Autoencoder](https://mlsanity.com/paper/2404.10211/) (2024)
- [Natural Language Querying System Through Entity Enrichment](https://mlsanity.com/paper/2410.15753/) (2024)
- [Detecting Undesired Process Behavior by Means of Retrieval Augmented Generation](https://mlsanity.com/paper/2505.22041/) (2025)

## Abstract (arXiv)

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.