Voila: Visual Anomaly Detection and Monitoring with Streaming Spatiotemporal Data

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 cl...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on visualization and computer graphics Vol. 24; no. 1; pp. 23 - 33
Main Authors Cao, Nan, Lin, Chaoguang, Zhu, Qiuhan, Lin, Yu-Ru, Teng, Xian, Wen, Xidao
Format Journal Article
LanguageEnglish
Published United States IEEE 01.01.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithm design and analysis
Anomalies
Anomaly detection
Data models
Data visualization
Interactive systems
Monitoring
Quantitative analysis
Spatiotemporal data
Spatiotemporal phenomena
Tensile stress
Visual Analysis
Visualization
Online AccessGet full text

Cover

More Information
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1077-2626
1941-0506
DOI:10.1109/TVCG.2017.2744419