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DataMeadow: a visual canvas for analysis of large-scale multivariate data

Niklas Elmqvist ; John Stasko ; Philippas Tsigas (Institutionen för data- och informationsteknik, Nätverk och system, Datakommunikation och distribuerade system (Chalmers))
Information Visualization (1473-8716). Vol. 7 (2008), 1, p. 18-33.
[Artikel, refereegranskad vetenskaplig]

Supporting visual analytics of multiple large-scale multidimensional data sets requires a high degree of interactivity and user control beyond the conventional challenges of visualizing such data sets. We present the DataMeadow, a visual canvas providing rich interaction for constructing visual queries using graphical set representations called DataRoses. A DataRose is essentially a starplot of selected columns in a data set displayed as multivariate visualizations with dynamic query sliders integrated into each axis. The purpose of the DataMeadow is to allow users to create advanced visual queries by iteratively selecting and filtering into the multidimensional data. Furthermore, the canvas provides a clear history of the analysis that can be annotated to facilitate dissemination of analytical results to stakeholders. A powerful direct manipulation interface allows for selection, filtering, and creation of sets, subsets, and data dependencies. We have evaluated our system using a qualitative expert review involving two visualization researchers. Results from this review are favorable for the new method.

Nyckelord: Multivariate data, Visual analytics, Parallel coordinates, Dynamic queries, Progressive analysis, Starplots

Special Issue devoted to selected papers of the 2007 IEEE Visual Analytics Science and Technology Symposium (VAST 2007).

Denna post skapades 2008-04-02.
CPL Pubid: 69818


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