Exploration of Linked Anomalies in Sensor Data for Suspicious Behavior Detection
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    Abstract:

    We present a visual analytics system to understand the operation data of a company, GAStech, from IEEE VAST Challenge 2016. The data include proximity data recording the locations and movements of employees, and heating, ventilation, and air conditioning (HVAC) data recording the environmental conditions in the building. Analyzing the data to detect the suspicious behaviors of some disgruntled employees is of special interest. Our system provides coordinated multiple views to visualize the proximity data and the HVAC data over time. Visual hints and comparisons are designed for users to identify abnormal patterns and compare them. Furthermore, the system automatically detects and correlates the anomalies in the data. We provide use cases to demonstrate the effectiveness of our system.

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Ian Turk, Matthew Sinda, Xin'an Zhou, Jun Tao, Chaoli Wang, Qi Liao. Exploration of Linked Anomalies in Sensor Data for Suspicious Behavior Detection. International Journal of Software and Informatics, 2016,10(3):0

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  • Received:
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  • Online: March 13,2017
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