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Generative Adversarial Network for Detecting Cyber Threats in Industrial Systems Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Employee Performance Analytics Approach Based on Anomaly Detection in User Activity

Authors : Aleksey Lukashin, Mikhail Popov, Dmitrii Timofeev, Igor Mikhalev

Published in: Proceedings of International Scientific Conference on Telecommunications, Computing and Control

Publisher: Springer Singapore

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Abstract

In this paper, we highlight the distinctive features and critical areas of analytical tool application for the employee performance analytics of user activity. We describe problems of applying data analytics methods and technologies to ensure employee performance analytics. We also discuss the use of user activity time-series data analysis methods and techniques to provide employee performance analytics and describe approaches for processing unstructured data from different sources of user activity for further analytics using anomaly detection methods. Finally, we introduce a new strategy of building features from hybrid data streams from different sources and compare it with current practices.

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Metadata
Title
Employee Performance Analytics Approach Based on Anomaly Detection in User Activity
Authors
Aleksey Lukashin
Mikhail Popov
Dmitrii Timofeev
Igor Mikhalev
Copyright Year
2021
Publisher
Springer Singapore
Book
Proceedings of International Scientific Conference on Telecommunications, Computing and Control

Print ISBN: 978-981-336-631-2

Electronic ISBN: 978-981-336-632-9

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-33-6632-9_28