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

  • Authors:

  • Niklas Elmqvist;John Stasko;Philippas Tsigas

  • Affiliations:

  • INRIA, Université Paris-Sud, France;School of Interactive Computing and the GVU Center, Georgia Institute of Technology, Atlanta, GA;Department of Computer Scinece & Engineering, Chalmers University of Technology, Gothenburg, Sweden

  • Venue:
  • Information Visualization - Special issue on visual analytics science and technology
  • Year:
  • 2008

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Abstract

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.