Synonyms
Data visualization; Information displays; Information visualization; Pixel-oriented visualization techniques; Visual data exploration; Visual data mining; Visualizing large data sets; Visualizing multidimensional and multivariate data
Definition
Dense pixel displays are a visual data exploration technique. Data exploration aims at analyzing large amounts of multidimensional data for detecting patterns and extracting hidden information. Human involvement is indispensable to carry out such a task, since human’s powerful perceptual abilities and domain knowledge are essential for defining interesting patterns and interpreting findings. Dense pixel displays support this task by an adequate visual representation of as much information as possible while avoiding aggregation of data values. Data is shown using every pixel of the display for representing one data point. Attributes of the data are mapped in separate sub-windows of the display, leaving one attribute for one sub-window....
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Anderson E. A semigraphical method for the analysis of complex problems. Proc Nat Acad Sci USA. 1957;43(10):923–7.
Ankerst M, Keim DA, Kriegel HP. Circle segments: a technique for visually exploring large multidimensional data sets. In: Proceedings of the IEEE Symposium on Visualization; 1996.
Brissom D. Hypergraphics: Visualizing complex relationships in art, science and technology (AAAS Selected Symposium; 24). Westview Press; 1979.
Chernoff H. The use of faces to represent points in k-dimensional space graphically. J Am Stat Assoc. 1973;68(342):361–8.
Cleveland WS. Visualizing data. Summit: Hobart Press; 1993.
Inselberg A. N-Dimensional Graphics Part I: Lines and Hyperplanes, IBM LA Science Center Report, # G320-2711; 1981.
Keim DA, Hao MC, Dayal U, Hsu M. Pixel bar charts: a visualization technique for very large multi-attribute data sets. Inf Visualization. 2001;1(1):20–34.
Keim DA, Kriegel HP, Ankerst M. Recursive pattern: a technique for visualizing very large amounts of data. In: Proceedings of the IEEE Symposium on Visualization; 1995. p. 279–86.
Keim DA, Nietzschmann T, Schelwies N, Schneidewind J, Schreck T, Zeigler H. A spectral visualization system for analyzing financial time series data. In: Proceedings of the Eighth Joint Eurographics/IEEE VGTC conference on Visualization; 2006. p. 195–202.
Keim DA, North SC, Panse C, Sips M. PixelMaps: a new visual data mining approach for analyzing large spatial data sets. In: Proceedings of the 2003 IEEE International Conference on Data Mining; 2003. p. 565–8.
Keim DA, Oelke D. Literature fingerprinting: a new method for visual literary analysis. In: Proceedings of the IEEE Symposium on Visual Analytics and Technology; 2007.
Langton JT, Prinz AA, Wittenberg DK, Hickey TJ. Leveraging layout with dimensional stacking and pixelization to facilitate feature discovery and directed queries. In: Proceedings of the Visual Information Expert Workshop; 2006.
Tufte ER. The visual display of quantitative information. Cheshire: Graphics Press; 1983.
Tufte ER. Envisioning information. Cheshire: Graphics Press; 1990.
Ziegler H, Nietzschmann T, Keim DA. Relevance driven visualization of financial performance measures. In: Proceedings of the Eurographics/IEEE-VGTC Symposium on Visualization; 2007. p. 19–26.
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Keim, D.A., Bak, P., Schäfer, M. (2018). Dense Pixel Displays. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_1131
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DOI: https://doi.org/10.1007/978-1-4614-8265-9_1131
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