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Limiting Result Cardinalities for Multidatabase Queries Using Histograms

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Advances in Databases (BNCOD 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2097))

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Abstract

Integrating, cleaning and analyzing data from heterogeneous sources is often complicated by the large amounts of data and its physical distribution which can result in poor query response time. One approach to speed up the processing is to reduce the cardinality of results – either by querying only the first tuples or by obtaining a sample for further processing. In this paper we address the processing of such queries in a multidatabase environment. We discuss implementations of the query operators, strategies for their placement in a query plan and particularly the usage of histograms for estimating attribute value distributions and result cardinalities in order to parameterize the operators.

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Research was supported by the grant FOR 345/1 from the DFG.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Magdeburg, P.O. Box 4120, D-39016, Magdeburg, Germany

    Kai-Uwe Sattler, Oliver Dunemann, Ingolf Geist & Gunter Saake

  2. Department of Computer Science, University of Munich, Oettingenstr. 67, D-80538, München, Germany

    Stefan Conrad

Authors
  1. Kai-Uwe Sattler
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  2. Oliver Dunemann
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  3. Ingolf Geist
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  4. Gunter Saake
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  5. Stefan Conrad
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Editor information

Editors and Affiliations

  1. Information Technology Department, CLRC Rutherford Appleton Laboratory, Chilton, Didcot, Oxfordshire, OX11 0QX, UK

    Brian Read

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© 2001 Springer-Verlag Berlin Heidelberg

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Sattler, KU., Dunemann, O., Geist, I., Saake, G., Conrad, S. (2001). Limiting Result Cardinalities for Multidatabase Queries Using Histograms. In: Read, B. (eds) Advances in Databases. BNCOD 2001. Lecture Notes in Computer Science, vol 2097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45754-2_10

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  • DOI: https://doi.org/10.1007/3-540-45754-2_10

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42265-5

  • Online ISBN: 978-3-540-45754-1

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