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Ordered and quantum treemaps: Making effective use of 2D space to display hierarchies
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Volume 21 ,  Issue 4  (October 2002) table of contents
Pages: 833 - 854  
Year of Publication: 2002
ISSN:0730-0301
Authors
Benjamin B. Bederson  University of Maryland, College Park, MD
Ben Shneiderman  University of Maryland, College Park, MD
Martin Wattenberg  IBM Research, Cambridge, MA
Publisher
ACM  New York, NY, USA
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ABSTRACT

Treemaps, a space-filling method for visualizing large hierarchical data sets, are receiving increasing attention. Several algorithms have been previously proposed to create more useful displays by controlling the aspect ratios of the rectangles that make up a treemap. While these algorithms do improve visibility of small items in a single layout, they introduce instability over time in the display of dynamically changing data, fail to preserve order of the underlying data, and create layouts that are difficult to visually search. In addition, continuous treemap algorithms are not suitable for displaying fixed-sized objects within them, such as images.This paper introduces a new "strip" treemap algorithm which addresses these shortcomings, and analyzes other "pivot" algorithms we recently developed showing the trade-offs between them. These ordered treemap algorithms ensure that items near each other in the given order will be near each other in the treemap layout. Using experimental evidence from Monte Carlo trials and from actual stock market data, we show that, compared to other layout algorithms, ordered treemaps are more stable, while maintaining relatively favorable aspect ratios of the constituent rectangles. A user study with 20 participants clarifies the human performance benefits of the new algorithms. Finally, we present quantum treemap algorithms, which modify the layout of the continuous treemap algorithms to generate rectangles that are integral multiples of an input object size. The quantum treemap algorithm has been applied to PhotoMesa, an application that supports browsing of large numbers of images.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Jungmeister, W.-A. and Turo, D. 1992. Adapting Treemaps to Stock Portfolio Visualization. Tech. Rep. CS-TR-2996, Computer Science Department, University of Maryland, College Park, MD.
 
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Sheldon, R. A. 1997. A First Course in Probability. Englewood Cliffs, NJ: Prentice Hall.
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Vernier, F. and Nigay, L. 2000. Modifiable treemaps containing variable-shaped units. In Proceedings of Extended Abstracts of IEEE Information Visualization (InfoVis 2000) New York: IEEE, pp. 28--35.
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CITED BY  36

Collaborative Colleagues:
Benjamin B. Bederson: colleagues
Ben Shneiderman: colleagues
Martin Wattenberg: colleagues