Extreme visualization: squeezing a billion records into a million pixels: Difference between revisions
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== Suitable Datatypes == | == Suitable Datatypes == | ||
Information visualizations are designed to deal with multi-dimensional and more importantly multi-variate data. | |||
In addation to | |||
* integer, | |||
* categorical, | |||
* real, | |||
* and nominal | |||
information visualizations often deal with even richer data types. | |||
The four types | |||
* multi-variate, | |||
* time series, | |||
* tree, | |||
* and network | |||
are tied to tasks such as finding clusters, gaps, outliers, trends, and relationships. | |||
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== | == Internal References == | ||
[http://infovis-wiki.net/index.php?title=Treemap Treemap] | [http://infovis-wiki.net/index.php?title=Treemap Treemap] | ||
[[category: techniques]] | [[category: techniques]] |
Revision as of 07:46, 26 May 2009
UNDER CONSTRUCTION
Authors
Short description
Suitable Datatypes
Information visualizations are designed to deal with multi-dimensional and more importantly multi-variate data.
In addation to
- integer,
- categorical,
- real,
- and nominal
information visualizations often deal with even richer data types.
The four types
- multi-variate,
- time series,
- tree,
- and network
are tied to tasks such as finding clusters, gaps, outliers, trends, and relationships.
Figures
Important citations
The purpose of visualization is insight, not pictures.
[Ben Shneiderman, 2008]
Evaluation
References
Extreme visualization: squeezing a billion records into a million pixels