Immersive Visualization for Abnormal Detection in Heterogeneous Data for On-site Decision Making

Mahfoud, Elias
Wegba, Kodzo
Li, Yuemeng
Han, Honglei
Lu, Aidong
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The latest advances in mixed reality promote new capabilities that allow head-mounted displays, such as Microsoft HoloLens, to visualize various data and information in a real physical environment. While such new features have great potential for new generations of visualization systems, they require fundamentally different visualization and interaction techniques that have not been well explored. This paper presents an immersive visualization approach for investigating abnormal events in heterogeneous, multi-source, and time-series sensor data collections in real-time on the site of the event. Our approach explores the essential components for an analyst to visualize complex data and explore hidden connections in mixed reality; it also combines automatic event detection algorithms to identify suspicious activities. We demonstrate our prototype system by using the developer version of Microsoft HoloLens and presenting case studies that require an analyst to investigate related data on site. We also discuss the limitations of the current infrastructure and potential applications for security visualization.
Interactive Visual Analytics and Visualization for Decision Making � Making Sense of Big Data, Abnormal Detection, Augmented Reality, Immersive Visualization, On-site Decision Making
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