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Genetic Algorithm for Materialized View Selection in Data Warehouse Environments

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DataWarehousing and Knowledge Discovery (DaWaK 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1676))

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Abstract

DataWarehouse applications use a large number of materialized views to assist a Data Warehouse to perform well. But how to select views to be materialized is challenging. Several heuristic algorithms have been proposed in the past to tackle with this problem. In this paper, we propose a completely different approach, Genetic Algorithm, to choose materialized views and demonstrate that it is practical and effective compared with heuristic approaches.

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Author information

Authors and Affiliations

  1. School of Computer Science, University College, UNSW ADFA, Canberra, ACT, 2600, Australia

    Chuan Zhang

  2. Spatial Information Systems, CMIS, CSIRO, GPO Box 664, Canberra, ACT, 2601, Australia

    Jian Yang

Authors
  1. Chuan Zhang
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  2. Jian Yang
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Editor information

Editors and Affiliations

  1. School of Computer and Information Science, University of South Australia, The Levels, Adelaide, Australia, 05

    Mukesh Mohania

  2. IFS, Technical University of Vienna, Resselgasse 3, A-1040, Vienna, Austria

    A Min Tjoa

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© 1999 Springer-Verlag Berlin Heidelberg

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Zhang, C., Yang, J. (1999). Genetic Algorithm for Materialized View Selection in Data Warehouse Environments. In: Mohania, M., Tjoa, A.M. (eds) DataWarehousing and Knowledge Discovery. DaWaK 1999. Lecture Notes in Computer Science, vol 1676. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48298-9_12

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  • DOI: https://doi.org/10.1007/3-540-48298-9_12

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66458-1

  • Online ISBN: 978-3-540-48298-7

  • eBook Packages: Springer Book Archive

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