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Erschienen in:
2022 | OriginalPaper | Buchkapitel
Log File Anomaly Detection Based on Process Mining Graphs
verfasst von : Sabrina Luftensteiner, Patrick Praher
Erschienen in: Database and Expert Systems Applications - DEXA 2022 Workshops
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Abstract
In process industry, it is quite common that manufacturing machines repeat a certain order of process steps consistently. These process steps are often declared in programs and have a linear workflow. During the production process, logs are generated for monitoring and further analysis, where not only production steps but also incidents and other deviations are logged. As the manual monitoring of such processes is quite time consuming and tedious, the demand for automatic anomaly and deviation detection is rising. A potential approach is the usage of Process Mining, whereat not all requirements are met. In this paper, we propose a new approach based on spectral gap analysis for the detection of anomalies in log files using the adjacency matrix generated by Process Mining techniques. Furthermore, the experiments section covers their application on a linear process and non-linear processes with deviating paths.