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Erschienen in:
Process Automation and Process Mining in Manufacturing Kapitel lesen Erstes Kapitel lesen

2021 | OriginalPaper | Buchkapitel

Graph Autoencoders for Business Process Anomaly Detection

verfasst von : Siyu Huo, Hagen Völzer, Prabhat Reddy, Prerna Agarwal, Vatche Isahagian, Vinod Muthusamy

Erschienen in: Business Process Management

Verlag: Springer International Publishing

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Abstract

We propose an approach to identify anomalies in business processes by building an anomaly detector using graph encodings of process event log data coupled with graph autoencoders. We evaluate the proposed approach with randomly mutated real event logs as well as synthetic data. The evaluation shows significant performance improvements (in terms of F1 score) over previous approaches, in particular with respect to other types of autoencoders that use flat encodings of the same data. The performance improvements are also stable under training and evaluation noise. Our approach is generic in that it requires no prior knowledge of the business process.

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Vorheriges Kapitel A Framework for Explainable Concept Drift Detection in Process Mining
Nächstes Kapitel Drivers and Barriers of the Digital Innovation Process – Case Study Insights from a German Public University
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Metadaten
Titel
Graph Autoencoders for Business Process Anomaly Detection
verfasst von
Siyu Huo
Hagen Völzer
Prabhat Reddy
Prerna Agarwal
Vatche Isahagian
Vinod Muthusamy
Copyright-Jahr
2021
Buch
Business Process Management

Print ISBN: 978-3-030-85468-3

Electronic ISBN: 978-3-030-85469-0

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-85469-0_26