Accessing and visualizing scientific spatiotemporal data

Daniel S. Katz, Attila Bergou, G. Bruce Berriman, Gary L. Block, Jim Collier, David W. Curkendall, John Good, Laura Husman, Joseph C. Jacob, Anastasia Laity, P. Peggy Li, Craig Miller, Tom Prince, Herb Siegel, Roy Williams

Research output: Contribution to journalConference article

Abstract

This paper discusses work done by JPL 's Parallel Applications Technologies Group in helping scientists access and visualize very large data sets through the use of multiple computing resources, such as parallel supercomputers, clusters, and grids. These tools do one or more of the following tasks: visualize local data sets for local users, visualize local data sets for remote users, and access and visualize remote data sets. The tools are used for various types of data, including remotely sensed image data, digital elevation models, astronomical surveys, etc. The paper attempts to pull some common elements out of these tools that may be useful for others who have to work with similarly large data sets.

Original languageEnglish (US)
Pages (from-to)107-110
Number of pages4
JournalProceedings of the International Conference on Scientific and Statistical Database Management, SSDBM
Volume16
StatePublished - Oct 25 2004
Externally publishedYes
EventProceedings - 16th International Conference on Scientific and Statistical Databse Management, SSDBM 2004 - Santorini Island, Greece
Duration: Jun 21 2004Jun 23 2004

ASJC Scopus subject areas

  • Software
  • Applied Mathematics

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  • Cite this

    Katz, D. S., Bergou, A., Berriman, G. B., Block, G. L., Collier, J., Curkendall, D. W., Good, J., Husman, L., Jacob, J. C., Laity, A., Li, P. P., Miller, C., Prince, T., Siegel, H., & Williams, R. (2004). Accessing and visualizing scientific spatiotemporal data. Proceedings of the International Conference on Scientific and Statistical Database Management, SSDBM, 16, 107-110.