Abstract:

In this paper, we present an interactive visualization method for set-valued attributes that maintains the advantages of item-oriented views and interactions found in parallel multivariate visualizations such as bargrams (equal-height histograms). The challenge is to accommodate rendering of an item when it appears multiple times in set-valued attribute views while at the same time preserving value-and item-based selection, brushing, and filtering. Such techniques can help users derive particular types of insights into data based on distributions and correlations of attribute values.