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  • Proceedings of the Hawaii International Conference on System Sciences

Multivariate Unsupervised Machine Learning for Anomaly Detection in Enterprise Applications

By: Daniel Elsner, Pouya Aleatrati Khosroshahi, Alan MacCormack and Robert Lagerström
  • Format:Electronic
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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.

Keywords

Big Data; Data Science And Analytics Management; Governance And Compliance; Organizational Systems And Technology; Anomaly Detection; Application Performance Management; Machine Learning; Enterprise Architecture

Citation

Elsner, Daniel, Pouya Aleatrati Khosroshahi, Alan MacCormack, and Robert Lagerström. "Multivariate Unsupervised Machine Learning for Anomaly Detection in Enterprise Applications." Proceedings of the Hawaii International Conference on System Sciences 52nd (2019): 5827–5836.
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About The Author

Alan D. MacCormack

Technology and Operations Management
→More Publications

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More from the Authors
  • Computer-Implemented Methods and Systems for Measuring, Estimating, and Managing Economic Outcomes and Technical Debt in Software Systems and Projects: US Patent 11,126,427 B2 By: Daniel J. Sturtevant, Carliss Baldwin, Alan MacCormack, Sunny Ahn and Sean Gilliland
  • A Note on Design Thinking By: Alan MacCormack, Caroline M. Elkins, Allison H. Mnookin, Leonard A. Schlesinger and Joyce J. Kim
  • Disrupting the Disruptors or Enhancing Them? How Blockchain Re‐Shapes Two‐Sided Platforms By: Daniel Trabucchi, Antonella Moretto, Tommaso Buganza and Alan MacCormack
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