Accessing and Visualizing Scientific Spatiotemporal Data

Authors:
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
Affiliations:
California Institute of Technology, USA;California Institute of Technology, USA;California Institute of Technology, USA;California Institute of Technology, USA;California Institute of Technology, USA;California Institute of Technology, USA;California Institute of Technology, USA;California Institute of Technology, USA;California Institute of Technology, USA;California Institute of Technology, USA;California Institute of Technology, USA;California Institute of Technology, USA;California Institute of Technology, USA;California Institute of Technology, USA;California Institute of Technology, USA
Venue:
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
Year:
2004

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Abstract

This paper discusses work done by JPL's ParallelApplications Technologies Group in helping scientistsaccess and visualize very large data sets through the useof multiple computing resources, such as parallelsupercomputers, clusters, and grids. These tools do oneor more of the following tasks: visualize local data setsfor local users, visualize local data sets for remote users,and access and visualize remote data sets. The tools areused for various types of data, including remotely sensedimage data, digital elevation models, astronomicalsurveys, etc. The paper attempts to pull some commonelements out of these tools that may be useful for otherswho have to work with similarly large data sets.