Abstract
Logs have been used in modern software solutions for development and maintenance purposes as they are able to represent a rich source of information for subsequent analysis. A line of research focuses on the application of artificial intelligence techniques on logs to predict system behavior and to perform anomaly detection. Successful industrial applications are rather sparse due to the lack of publicly available log datasets. To fill this gap, we developed a method to synthetically generate a log dataset, which resembles a linear program execution log file. In this paper, the method is described as well as existing datasets are discussed. The generated dataset should enable a possibility for researcher to have a common base for new approaches.
The research reported in this paper has been funded by the Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK), the Federal Ministry for Digital and Economic Affairs (BMDW), and the Province of Upper Austria in the frame of the COMET - Competence Centers for Excellent Technologies Programme managed by Austrian Research Promotion Agency FFG.
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Luftensteiner, S., Praher, P. (2022). A Synthetic Dataset for Anomaly Detection of Machine Behavior. In: Kotsis, G., et al. Database and Expert Systems Applications - DEXA 2022 Workshops. DEXA 2022. Communications in Computer and Information Science, vol 1633. Springer, Cham. https://doi.org/10.1007/978-3-031-14343-4_40
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DOI: https://doi.org/10.1007/978-3-031-14343-4_40
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