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Data Visualization pp 257–272Cite as

A Data Model for Adaptive Multi-Resolution Scientific Data

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Part of the book series: The Springer International Series in Engineering and Computer Science ((SECS,volume 713))

Abstract

Representing data using multiresolution is a valuable tool for the interactive exploration of very large datasets. Current multiresolution tools are written specifically for a single kind of multiresolution data. As a step toward developing general purpose multiresolution tools, we present here a model that represents a wide range of multiresolution data within a single paradigm. In addition, our model provides support for working with multiresolution data in a distributed environment.

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Author information

Authors and Affiliations

  1. Computer Science Department, University of New Hampshire, USA

    Philip J. Rhodes, R. Daniel Bergeron & Ted M. Sparr

Authors
  1. Philip J. Rhodes
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  2. R. Daniel Bergeron
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  3. Ted M. Sparr
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Editor information

Editors and Affiliations

  1. Delft University of Technology, The Netherlands

    Frits H. Post

  2. Arizona State University, USA

    Gregory M. Nielson

  3. University Grenoble I/GRAVIR/IMAG, France

    Georges-Pierre Bonneau

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© 2003 Springer Science+Business Media Dordrecht

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Rhodes, P.J., Bergeron, R.D., Sparr, T.M. (2003). A Data Model for Adaptive Multi-Resolution Scientific Data. In: Post, F.H., Nielson, G.M., Bonneau, GP. (eds) Data Visualization. The Springer International Series in Engineering and Computer Science, vol 713. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1177-9_18

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  • DOI: https://doi.org/10.1007/978-1-4615-1177-9_18

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-5430-7

  • Online ISBN: 978-1-4615-1177-9

  • eBook Packages: Springer Book Archive

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