HDF5-FastQuery: Accelerating Complex Queries on HDF Datasets usingFast Bitmap Indices

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Large scale scientific data is often stored in scientific data formats such as FITS, netCDF and HDF. These storage formats are of particular interest to the scientific user community since they provide multi-dimensional storage and retrieval. However, one of the drawbacks of these storage formats is that they do not support semantic indexing which is important for interactive data analysis where scientists look for features of interests such as ''Find all supernova explosions where energy > 10{sup 5} and temperature > 10{sup 6}''. In this paper we present a novel approach called HDF5-FastQuery to accelerate the data access of large ... continued below

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Gosink, Luke; Shalf, John; Stockinger, Kurt; Wu, Kesheng & Bethel,Wes March 30, 2006.

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Large scale scientific data is often stored in scientific data formats such as FITS, netCDF and HDF. These storage formats are of particular interest to the scientific user community since they provide multi-dimensional storage and retrieval. However, one of the drawbacks of these storage formats is that they do not support semantic indexing which is important for interactive data analysis where scientists look for features of interests such as ''Find all supernova explosions where energy > 10{sup 5} and temperature > 10{sup 6}''. In this paper we present a novel approach called HDF5-FastQuery to accelerate the data access of large HDF5 files by introducing multi-dimensional semantic indexing. Our implementation leverages an efficient indexing technology called bitmap indexing that has been widely used in the database community. Bitmap indices are especially well suited for interactive exploration of large-scale read only data. Storing the bitmap indices into the HDF5 file has the following advantages: (a) Significant performance speedup of accessing subsets of multi-dimensional data and (b) portability of the indices across multiple computer platforms. We will present an API that simplifies the execution of queries on HDF5 files for general scientific applications and data analysis. The design is flexible enough to accommodate the use of arbitrary indexing technology for semantic range queries. We will also provide a detailed performance analysis of HDF5-FastQuery for both synthetic and scientific data. The results demonstrate that our proposed approach for multi-dimensional queries is up to a factor of 2 faster than HDF5.

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  • International Conference on Scientific andStatistical Database Management (SSDBM 2006), Vienna, Austria, July 3 -5,2006

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  • Report No.: LBNL--59602
  • Grant Number: DE-AC02-05CH11231
  • Office of Scientific & Technical Information Report Number: 881620
  • Archival Resource Key: ark:/67531/metadc873483

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

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  • March 30, 2006

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  • Sept. 21, 2016, 2:29 a.m.

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  • Sept. 22, 2017, 3:07 p.m.

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Gosink, Luke; Shalf, John; Stockinger, Kurt; Wu, Kesheng & Bethel,Wes. HDF5-FastQuery: Accelerating Complex Queries on HDF Datasets usingFast Bitmap Indices, article, March 30, 2006; Berkeley, California. (digital.library.unt.edu/ark:/67531/metadc873483/: accessed October 17, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.