Using bitmap index for interactive exploration of large datasets

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Many scientific applications generate large spatio-temporal datasets. A common way of exploring these datasets is to identify and track regions of interest. Usually these regions are defined as contiguous sets of points whose attributes satisfy some user defined conditions, e.g. high temperature regions in a combustion simulation. At each time step, the regions of interest may be identified by first searching for all points that satisfy the conditions and then grouping the points into connected regions. To speed up this process, the searching step may use a tree based indexing scheme, such as a kd-tree or an octree. However, these ... continued below

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Wu, Kesheng; Koegler, Wendy; Chen, Jacqueline & Shoshani, Arie April 24, 2003.

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Many scientific applications generate large spatio-temporal datasets. A common way of exploring these datasets is to identify and track regions of interest. Usually these regions are defined as contiguous sets of points whose attributes satisfy some user defined conditions, e.g. high temperature regions in a combustion simulation. At each time step, the regions of interest may be identified by first searching for all points that satisfy the conditions and then grouping the points into connected regions. To speed up this process, the searching step may use a tree based indexing scheme, such as a kd-tree or an octree. However, these indices are efficient only if the searches are limited to one or a small number of selected attributes. Scientific datasets often contain hundreds of attributes and scientists frequently study these attributes incomplex combinations, e.g. finding regions of high temperature yet low shear rate and pressure. Bitmap indexing is an efficient method for searching on multiple criteria simultaneously. We apply a bitmap compression scheme to reduce the size of the indices. In addition, we show that the compressed bitmaps can be used efficiently to perform the region growing and the region tracking operations. Analyses show that our approach scales well and our tests on two datasets from simulation of the auto ignition process show impressive performance.

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OSTI as DE00813383

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  • 15th International Conference on Scientific and Statistical Database Management, Cambridge, MA (US), 07/09/2003--07/11/2003

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  • Report No.: LBNL--52535
  • Grant Number: AC03-76SF00098
  • Office of Scientific & Technical Information Report Number: 813383
  • Archival Resource Key: ark:/67531/metadc737929

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  • April 24, 2003

Added to The UNT Digital Library

  • Oct. 18, 2015, 6:40 p.m.

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  • April 4, 2016, 3:52 p.m.

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Wu, Kesheng; Koegler, Wendy; Chen, Jacqueline & Shoshani, Arie. Using bitmap index for interactive exploration of large datasets, article, April 24, 2003; Berkeley, California. (digital.library.unt.edu/ark:/67531/metadc737929/: accessed October 20, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.