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A Graph Algebra for Scalable Visual Analytics
July-Aug. 2012 (vol. 32 no. 4)
pp. 26-33
Anna A. Shaverdian, University of Michigan
Hao Zhou, University of Michigan
George Michailidis, University of Michigan
Hosagrahar V. Jagadish, University of Michigan
Visual analytics (VA), which combines analytical techniques with advanced visualization features, is fast becoming a standard tool for extracting information from graph data. Researchers have developed many tools for this purpose, suggesting a need for formal methods to guide these tools' creation. Increased data demands on computing requires redesigning VA tools to consider performance and reliability in the context of analysis of exascale datasets. Furthermore, visual analysts need a way to document their analyses for reuse and results justification. A VA graph framework encapsulated in a graph algebra helps address these needs. Its atomic operators include selection and aggregation. The framework employs a visual operator and supports dynamic attributes of data to enable scalable visual exploration of data.

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Index Terms:
Data visualization,Visual analytics,Algebra,Image color analysis,extreme-scale visual analytics,Data visualization,Visual analytics,Algebra,Image color analysis,computer graphics,Data visualization,Visual analytics,Algebra,Xenon,Image color analysis,Educational institutions,graph algebra,visual analytics,exascale
Citation:
Anna A. Shaverdian, Hao Zhou, George Michailidis, Hosagrahar V. Jagadish, "A Graph Algebra for Scalable Visual Analytics," IEEE Computer Graphics and Applications, vol. 32, no. 4, pp. 26-33, July-Aug. 2012, doi:10.1109/MCG.2012.62
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