Efficient Joins with Compressed Bitmap Indexes

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We present a new class of adaptive algorithms that use compressed bitmap indexes to speed up evaluation of the range join query in relational databases. We determine the best strategy to process a join query based on a fast sub-linear time computation of the join selectivity (the ratio of the number of tuples in the result to the total number of possible tuples). In addition, we use compressed bitmaps to represent the join output compactly: the space requirement for storing the tuples representing the join of two relations is asymptotically bounded by min(h; n . cb), where h is the ... continued below

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Division, Computational Research; Madduri, Kamesh & Wu, Kesheng August 19, 2009.

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We present a new class of adaptive algorithms that use compressed bitmap indexes to speed up evaluation of the range join query in relational databases. We determine the best strategy to process a join query based on a fast sub-linear time computation of the join selectivity (the ratio of the number of tuples in the result to the total number of possible tuples). In addition, we use compressed bitmaps to represent the join output compactly: the space requirement for storing the tuples representing the join of two relations is asymptotically bounded by min(h; n . cb), where h is the number of tuple pairs in the result relation, n is the number of tuples in the smaller of the two relations, and cb is the cardinality of the larger column being joined. We present a theoretical analysis of our algorithms, as well as experimental results on large-scale synthetic and real data sets. Our implementations are efficient, and consistently outperform well-known approaches for a range of join selectivity factors. For instance, our count-only algorithm is up to three orders of magnitude faster than the sort-merge approach, and our best bitmap index-based algorithm is 1.2x-80x faster than the sort-merge algorithm, for various query instances. We achieve these speedups by exploiting several inherent performance advantages of compressed bitmap indexes for join processing: an implicit partitioning of the attributes, space-efficiency, and tolerance of high-cardinality relations.

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  • The 18th ACM Conference on Information and Knowledge Management , Hong Kong, China, November 2-6, 2009

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  • Report No.: LBNL-3115E
  • Grant Number: DE-AC02-05CH11231
  • Office of Scientific & Technical Information Report Number: 982896
  • Archival Resource Key: ark:/67531/metadc1012114

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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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  • August 19, 2009

Added to The UNT Digital Library

  • Oct. 14, 2017, 8:36 a.m.

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  • Oct. 17, 2017, 8:12 p.m.

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Division, Computational Research; Madduri, Kamesh & Wu, Kesheng. Efficient Joins with Compressed Bitmap Indexes, article, August 19, 2009; Berkeley, California. (digital.library.unt.edu/ark:/67531/metadc1012114/: accessed December 12, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.