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Unsupervised Anomaly Detection in Noisy Business Process Event Logs Using Denoising Autoencoders

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References

2004Workflow mining: discovering process models from event logs2,206 cited
2012Process Mining Manifesto1,209 cited
2007Conformance checking of processes based on monitoring real behavior1,119 cited
2006Process-aware information systems: bridging people and software through process technology617 cited
2005Process‐Aware Information Systems583 cited
2009Anomaly Detection Using Process Mining106 cited

Cited by

2017Detecting Concept Drift in Processes using Graph Metrics on Process Graphs48 cited
2021Natural language-based detection of semantic execution anomalies in event logs37 cited
2023GRASPED: A GRU-AE Network Based Multi-Perspective Business Process Anomaly Detection Model37 cited
2021Graph Autoencoders for Business Process Anomaly Detection37 cited
2024GAMA: A multi-graph-based anomaly detection framework for business processes via graph neural networks28 cited
2021Anomaly detection in business processes logs using social network analysis22 cited
2024Survey and Benchmark of Anomaly Detection in Business Processes21 cited
2023WAKE: A Weakly Supervised Business Process Anomaly Detection Framework via a Pre-Trained Autoencoder21 cited

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2020

Analysis of Language Inspired Trace Representation for Anomaly Detection

20 cited

2022Demystifying Noise and Outliers in Event Logs: Review and Future Directions19 cited