LSTM-based Anomaly Detection of Process Instances: Benchmark and Tweaks

Publication

Johannes Lahann; Peter Pfeiffer; Peter Fettke

In: Proceedings of the 4th International Conference on Process Mining (Workshops). International Conference on Process Mining (ICPM-2022), Process Mining Workshops, October 23-28, Bolzano, Italy, Lecture Notes in Business Information Processing, Springer, 2023.

Abstract

Anomaly detection can identify deviations in event logs and allows businesses to infer inconsistencies, bottlenecks, and optimization opportunities in their business processes. In recent years, various anomaly detection algorithms for business processes have been proposed based on either process discovery or machine learning algorithms. While there are apparent differences between the existing approaches, it is often unclear how they perform in comparison. Furthermore, deep learning research in other domains has shown that advancements did not solely come from improved model architecture but are often due to minor training procedure refinements. For this reason, this paper aims to set up a broad benchmark and establish a baseline for deep learning-based anomaly detection of process instances. To this end, we introduce a simple LSTM-based anomaly detector utilizing a collection of minor refinements and compare it with existing approaches. The results suggest that the proposed method can significantly outperform the existing approaches on a large number of event logs consistently.

Projekte

APPaM - Automated Process Planning and Mining

LSTM-based Anomaly Detection of Process Instances: Benchmark and Tweaks

Abstract

Projekte

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