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- W2743175062 abstract "Detection of anomalies in employees’ movement represents an area of considerable interest for cyber-physical security applications. In the paper the visual analytics approach to detection of the spatiotemporal patterns and anomalies in organization stuff movement is proposed. The key elements of the approach are interactive self-organizing maps used to detect groups of employees with similar behavior and heat map applied to detect anomalies. They are supported by a set of the interactive interconnected visual models aimed to present spatial and temporal route patterns. We demonstrate our approach with an application to the VAST MiniChallenge-2 2016 data set, which describes movement of the employees within organization building." @default.
- W2743175062 created "2017-08-17" @default.
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- W2743175062 date "2017-01-01" @default.
- W2743175062 modified "2023-09-27" @default.
- W2743175062 title "Visualization-Driven Approach to Anomaly Detection in the Movement of Critical Infrastructure" @default.
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- W2743175062 doi "https://doi.org/10.1007/978-3-319-65127-9_5" @default.
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