Exploration of Linked Anomalies in Sensor Data for Suspicious Behavior Detection

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