Dynamic Sharing of Large-Scale Visualization

Authors:
Jian Huang;Huadong Liu;Micah Beck;Andrew Gaston;Jinzhu Gao;Terry Moore
Affiliations:
University of Tennessee, Knoxville;University of Tennessee, Knoxville;University of Tennessee, Knoxville;University of Tennessee, Knoxville;Oak Ridge National Laboratory;University of Tennessee, Knoxville
Venue:
IEEE Computer Graphics and Applications
Year:
2007

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Abstract

There has been a recent trend for large groups of users to collaborate on large data sets across geographical distances. In this context, if users could share and modify a visualization over the standard Internet without requiring local replication of the data, the visualization community would have an even greater impact on the conduct of today's research. Various remote and collaborative visualization approaches have been proposed. This article presents a different viewpoint. The authors believe that for dynamic sharing of large-scale visualization, distributed heterogeneous resources that are free, unscheduled, and unreserved could serve as a fundamental and sufficient platform to support large groups of users--with greater potential usability, scalability, and cost efficiency.