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Issue No. 01 - Jan. (2018 vol. 24)
ISSN: 1077-2626
pp: 23-33
Nan Cao , Intelligent Big Data Visualization (iDVx) LabTongji University
Chaoguang Lin , Intelligent Big Data Visualization (iDVx) LabTongji University
Qiuhan Zhu , Intelligent Big Data Visualization (iDVx) LabTongji University
Yu-Ru Lin , University of Pittsburgh
Xian Teng , University of Pittsburgh
Xidao Wen , University of Pittsburgh
The increasing availability of spatiotemporal data continuously collected from various sources provides new opportunities for a timely understanding of the data in their spatial and temporal context. Finding abnormal patterns in such data poses significant challenges. Given that there is often no clear boundary between normal and abnormal patterns, existing solutions are limited in their capacity of identifying anomalies in large, dynamic and heterogeneous data, interpreting anomalies in their multifaceted, spatiotemporal context, and allowing users to provide feedback in the analysis loop. In this work, we introduce a unified visual interactive system and framework, Voila, for interactively detecting anomalies in spatiotemporal data collected from a streaming data source. The system is designed to meet two requirements in real-world applications, i.e., online monitoring and interactivity. We propose a novel tensor-based anomaly analysis algorithm with visualization and interaction design that dynamically produces contextualized, interpretable data summaries and allows for interactively ranking anomalous patterns based on user input. Using the “smart city” as an example scenario, we demonstrate the effectiveness of the proposed framework through quantitative evaluation and qualitative case studies.
Spatiotemporal phenomena, Data visualization, Anomaly detection, Visualization, Tensile stress, Algorithm design and analysis, Data models

N. Cao, C. Lin, Q. Zhu, Y. Lin, X. Teng and X. Wen, "Voila: Visual Anomaly Detection and Monitoring with Streaming Spatiotemporal Data," in IEEE Transactions on Visualization & Computer Graphics, vol. 24, no. 1, pp. 23-33, 2018.
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