Pixel Bar Charts : A Visualization Technique for Very Large Multi-Attribute Data Sets

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2002
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Hao, Ming C.
Dayal, Umeshwar
Hsu, Meichun
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Information visualization. 2002, 1(1), pp. 20-34. ISSN 1473-8716. eISSN 1473-8724. Available under: doi: 10.1057/palgrave.ivs.9500003
Zusammenfassung

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 ecommerce 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.

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004 Informatik
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Information visualization, multi-dimensional data visualization, visual data exploration and data mining, very large multi-attribute data sets
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ISO 690KEIM, Daniel A., Ming C. HAO, Umeshwar DAYAL, Meichun HSU, 2002. Pixel Bar Charts : A Visualization Technique for Very Large Multi-Attribute Data Sets. In: Information visualization. 2002, 1(1), pp. 20-34. ISSN 1473-8716. eISSN 1473-8724. Available under: doi: 10.1057/palgrave.ivs.9500003
BibTex
@article{Keim2002Pixel-5464,
  year={2002},
  doi={10.1057/palgrave.ivs.9500003},
  title={Pixel Bar Charts : A Visualization Technique for Very Large Multi-Attribute Data Sets},
  number={1},
  volume={1},
  issn={1473-8716},
  journal={Information visualization},
  pages={20--34},
  author={Keim, Daniel A. and Hao, Ming C. and Dayal, Umeshwar and Hsu, Meichun}
}
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