Multivariate Unsupervised Machine Learning for Anomaly Detection in Enterprise Applications
Multivariate Unsupervised Machine Learning for Anomaly Detection in Enterprise Applications
dc.contributor.author | Elsner, Daniel | |
dc.contributor.author | Aleatrati Khosroshahi, Pouya | |
dc.contributor.author | MacCormack, Alan D. | |
dc.contributor.author | Lagerström, Robert | |
dc.date.accessioned | 2019-01-03T00:42:38Z | |
dc.date.available | 2019-01-03T00:42:38Z | |
dc.date.issued | 2019-01-08 | |
dc.description.abstract | Existing application performance management (APM) solutions lack robust anomaly detection capabilities and root cause analysis techniques, that do not require manual efforts and domain knowledge. In this paper, we develop a density-based unsupervised machine learning model to detect anomalies within an enterprise application, based upon data from multiple APM systems. The research was conducted in collaboration with a European automotive company, using two months of live application data. We show that our model detects abnormal system behavior more reliably than a commonly used outlier detection technique and provides information for detecting root causes. | |
dc.format.extent | 10 pages | |
dc.identifier.doi | 10.24251/HICSS.2019.703 | |
dc.identifier.isbn | 978-0-9981331-2-6 | |
dc.identifier.uri | http://hdl.handle.net/10125/60018 | |
dc.language.iso | eng | |
dc.relation.ispartof | Proceedings of the 52nd Hawaii International Conference on System Sciences | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.subject | Big Data, Data Science and Analytics Management, Governance and Compliance | |
dc.subject | Organizational Systems and Technology | |
dc.subject | Anomaly Detection, Application Performance Management, Data Analytics, Enterprise Architecture, Machine Learning | |
dc.title | Multivariate Unsupervised Machine Learning for Anomaly Detection in Enterprise Applications | |
dc.type | Conference Paper | |
dc.type.dcmi | Text |
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