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Scalable Representation

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Scientific Visualization

Part of the book series: Mathematics and Visualization ((MATHVISUAL))

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Abstract

Although the amount and variety of data being generated is increased dramatically, the capabilities of data visualization, analysis, and discovery solutions have not been improved accordingly with the explosive rate of data production. One reason is that storage and processing at the level of raw data require supercomputer scale resources. The other is that working at the level of raw data prevents effective human comprehension while exploring and solving most problems. Here we show several approaches to scalable functional representations. Encoding, abstraction, and analysis at multiple scales of representations are a common approach in many scientific disciplines and provides a promising approach to harness our expanding digital universe.

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Author information

Authors and Affiliations

  1. Purdue University, West Lafayette, USA

    Yun Jang

  2. Sejong University, Seoul, South Korea

    Yun Jang

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  1. Yun Jang
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Correspondence to Yun Jang .

Editor information

Editors and Affiliations

  1. School of Computing Scientific Computing and Imaging Institu, University of Utah, Salt Lake City, Utah, USA

    Charles D. Hansen

  2. e-Research Centre, University of Oxford, Oxford, United Kingdom

    Min Chen

  3. School of Computing Scientific Computing and Imaging Institu, University of Utah, Salt Lake City, Utah, USA

    Christopher R. Johnson

  4. Department of Computer Science, Stony Brook University, New York, New York, USA

    Arie E. Kaufman

  5. Fachbereich Informatik, Technische Universität Kaiserslautern, Kaiserslautern, Germany

    Hans Hagen

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© 2014 Springer-Verlag London

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Jang, Y. (2014). Scalable Representation. In: Hansen, C., Chen, M., Johnson, C., Kaufman, A., Hagen, H. (eds) Scientific Visualization. Mathematics and Visualization. Springer, London. https://doi.org/10.1007/978-1-4471-6497-5_30

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