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Multivariate Unsupervised Machine Learning for Anomaly Detection in Enterprise Applications
|Title:||Multivariate Unsupervised Machine Learning for Anomaly Detection in Enterprise Applications|
Aleatrati Khosroshahi, Pouya
MacCormack, Alan D.
|Keywords:||Big Data, Data Science and Analytics Management, Governance and Compliance|
Organizational Systems and Technology
Anomaly Detection, Application Performance Management, Data Analytics, Enterprise Architecture, Machine Learning
|Date Issued:||08 Jan 2019|
|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.|
|Rights:||Attribution-NonCommercial-NoDerivatives 4.0 International|
|Appears in Collections:||
Big Data, Data Science and Analytics Management, Governance and Compliance|
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