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16th IEEE Visualization 2005 (VIS 2005)   p. 24
Distributed Data Management for Large Volume Visualization
Jinzhu Gao, Oak Ridge National Lab
Jian Huang, The Univ. of Tennessee
C. Ryan Johnson, The Univ. of Tennessee
Scott Atchley, The Univ. of Tennessee
James Arthur Kohl, Oak Ridge National Lab

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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/VIS.2005.23
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Abstract
We propose a distributed data management scheme for large data visualization that emphasizes efficient data sharing and access. To minimize data access time and support users with a variety of local computing capabilities, we introduce an adaptive data selection method based on an "Enhanced Time-Space Partitioning" (ETSP) tree that assists with effective visibility culling, as well as multiresolution data selection. By traversing the tree, our data management algorithm can quickly identify the visible regions of data, and, for each region, adaptively choose the lowest resolution satisfying userspecified error tolerances. Only necessary data elements are accessed and sent to the visualization pipeline. To further address the issue of sharing large-scale data among geographically distributed collaborative teams, we have designed an infrastructure for integrating our data management technique with a distributed data storage system provided by Logistical Networking (LoN). Data sets at different resolutions are generated and uploaded to LoN for wide-area access. We describe a parallel volume rendering system that verifies the effectiveness of our data storage, selection and access scheme.
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Index Terms- large data visualization, distributed storage, logistical networking, visibility culling, volume rendering, multiresolution rendering Citation:  Jinzhu Gao, Jian Huang, C. Ryan Johnson, Scott Atchley, James Arthur Kohl, "Distributed Data Management for Large Volume Visualization," vis, p. 24,  16th IEEE Visualization 2005 (VIS 2005),  2005