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Voila: Visual Anomaly Detection and Monitoring with Streaming Spatiotemporal Data

Cao, Nan and Lin, Chaoguang and Zhu, Qiuhan and Lin, Yu-Ru and Teng, Xian and Wen, Xian (2017) Voila: Visual Anomaly Detection and Monitoring with Streaming Spatiotemporal Data. IEEE Transactions on Visualization and Computer Graphics, 24 (1). pp. 23-33. ISSN 1077-2626


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.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Cao, Nan
Lin, Chaoguang
Zhu, Qiuhan
Lin, Yu-Ruyurulin@pitt.eduYURULIN
Teng, Xian
Wen, Xian
ContributionContributors NameEmailPitt UsernameORCID
Date: 30 August 2017
Journal or Publication Title: IEEE Transactions on Visualization and Computer Graphics
Volume: 24
Number: 1
Publisher: IEEE
Page Range: pp. 23-33
DOI or Unique Handle: 10.1109/tvcg.2017.2744419
Schools and Programs: School of Computing and Information > Information Science
Refereed: Yes
ISSN: 1077-2626
Article Type: Research Article
Date Deposited: 05 Jul 2018 19:46
Last Modified: 17 Mar 2020 19:54


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