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Accurate Cost Estimation Using Distribution-Based Cardinality Estimates for Multi-dimensional Queries

  • Conference paper
Scientific and Statistical Database Management (SSDBM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6809))

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Abstract

Cardinality estimation is crucial for query optimization. The optimizer uses cardinality estimates to compute the query-plan costs. Histograms are one of the most popular data structures used for cardinality estimation [2]. Because histograms compress the data set, the cardinality estimates issued are not exact.We model these estimates as random variables, and denote the cardinality of query q by card(q).

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References

  1. Bruno, N., Chaudhuri, S., Gravano, L.: STHoles: a multidimensional workload-aware histogram. SIGMOD Record (2001)

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  2. Ioannidis, Y.: The history of histograms (abridged). In: VLDB (2003)

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Author information

Authors and Affiliations

  1. Institute for Program Structures and Data Organization (IPD), Karlsruhe Institute of Technology (KIT), Germany

    Andranik Khachatryan & Klemens Böhm

Authors
  1. Andranik Khachatryan
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  2. Klemens Böhm
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Editor information

Editors and Affiliations

  1. The Evergreen State College, 98505, Olympia, WA, USA

    Judith Bayard Cushing

  2. CNRI and University of Virginia, 22908, Charlottesville, VA, USA

    James French

  3. Gonzaga University, 99258, Spokane, WA, USA

    Shawn Bowers

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© 2011 Springer-Verlag Berlin Heidelberg

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Khachatryan, A., Böhm, K. (2011). Accurate Cost Estimation Using Distribution-Based Cardinality Estimates for Multi-dimensional Queries. In: Bayard Cushing, J., French, J., Bowers, S. (eds) Scientific and Statistical Database Management. SSDBM 2011. Lecture Notes in Computer Science, vol 6809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22351-8_46

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  • DOI: https://doi.org/10.1007/978-3-642-22351-8_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22350-1

  • Online ISBN: 978-3-642-22351-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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