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¹Hewlett-Packard Research Labs, Palo Alto, California, USA

²CommerceOne, Pleasanton, California, USA

³AT&T Research Labs, Florham Park, NJ, USA

⁴University of Constance, Germany

Correspondence to: Daniel A Keim, AT&T Shannon Research Lab, 180 Park Avenue, P.O. Box 971, Florham Park, NJ 07932, U.S.A. Tel: +1 973 360 8482; Fax: +1 973 360 8077. E-mail: Keim@research.att.com

^†Portions reprinted, with permission from Keim et al.²⁵ Ó2001 IEEE

Simple presentation graphics are intuitive and easy-to-use, but show only highly aggregated data presenting only a very small number of data values (as in the case of bar charts) and may have a high degree of overlap occluding a significant portion of the data values (as in the case of the x-y plots). In this article, the authors therefore propose a generalization of traditional bar charts and x-y plots, which allows the visualization of large amounts of data. The basic idea is to use the pixels within the bars to present detailed information of the data records. The so-called pixel bar charts retain the intuitiveness of traditional bar charts while allowing very large data sets to be visualized in an effective way. It is shown that, for an effective pixel placement, a complex optimization problem has to be solved. The authors then present an algorithm which efficiently solves the problem. The application to a number of real-world e-commerce data sets shows the wide applicability and usefulness of this new idea, and a comparison to other well-known visualization techniques (parallel coordinates and spiral techniques) shows a number of clear advantages.

Information Visualization (2002) 1, 20-34. DOI: 10.1057/palgrave/ivs/9500003

Information visualization; multi-dimensional data visualization; visual data exploration and data mining; very large multi-attribute data sets