Pixel bar charts: a visualization technique for very large multi-attribute data sets

  • Authors:

  • Daniel A. Keim;Ming C Hao;Umesh Dayal;Meichun Hsu

  • Affiliations:

  • Hewlett-Packard Research Labs, Palo Alto, California and AT&T Research Labs, Florham Park, NJ and University of Constance, Germany;Hewlett-Packard Research Labs, Palo Alto, California;Hewlett-Packard Research Labs, Palo Alto, California;Hewlett-Packard Research Labs, Palo Alto, California and CommerceOne, Pleasanton, California

  • Venue:
  • Information Visualization
  • Year:
  • 2002

Quantified Score

Hi-index 0.00

Visualization

Abstract

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.