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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 (2018) Voila: Visual Anomaly Detection and Monitoring with Streaming Spatiotemporal Data. pp. 23-33.

Abstract

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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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Cao, Nan
Lin, Chaoguang
Zhu, Qiuhan
Lin, Yu-Ruyurulin@pitt.eduYURULIN
Teng, Xian
Wen, Xian
Contributors:
ContributionContributors NameEmailPitt UsernameORCID
CorrespondentLin, Yu-RuUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date: January 2018
Volume: 24
Number: 1
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
Article Type: Research Article
Date Deposited: 05 Jul 2018 19:46
Last Modified: 05 Jul 2018 19:46
URI: http://d-scholarship.pitt.edu/id/eprint/34710

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