Dynamic queries for information exploration: an implementation and evaluation
CHI '92 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
UIST '94 Proceedings of the 7th annual ACM symposium on User interface software and technology
FOCUS: the interactive table for product comparison and selection
Proceedings of the 9th annual ACM symposium on User interface software and technology
Multidimensional information visualization through sliding rods
AVI '00 Proceedings of the working conference on Advanced visual interfaces
Parallel bargrams for consumer-based information exploration and choice
Proceedings of the 14th annual ACM symposium on User interface software and technology
Selecting One from Many: The Development of a Scalable Visualization Tool
HCC '01 Proceedings of the IEEE 2001 Symposia on Human Centric Computing Languages and Environments (HCC'01)
Pixel bar charts: a visualization technique for very large multi-attribute data sets
Information Visualization
Parallel Sets: Visual Analysis of Categorical Data
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
Information Visualization: Design for Interaction (2nd Edition)
Information Visualization: Design for Interaction (2nd Edition)
Interactive Visual Analysis of Set-Typed Data
IEEE Transactions on Visualization and Computer Graphics
Visualizing set-valued attributes in parallel with equal-height histograms
Proceedings of the International Working Conference on Advanced Visual Interfaces
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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 renderings 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.