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Efficient Evaluation of Partially-Dimensional Range Queries Using Adaptive R*-tree

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Database and Expert Systems Applications (DEXA 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4080))

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Abstract

This paper is about how to efficiently evaluate partially-dimensional range queries, which are often used in many actual applications. If the existing multidimensional indices are employed to evaluate partially-dimensional range queries, then a great deal of information that is irrelevant to the queries also has to be read from disk. A modification of R*-tree is described in this paper to ameliorate such a situation. Discussions and experiments indicate that the proposed modification can clearly improve the performance of partially-dimensional range queries, especially for large datasets.

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Author information

Authors and Affiliations

  1. Graduate School of Information Science and Electrical Engineering, Kyushu University, Japan

    Yaokai Feng

  2. Department of Information Network Engineering, Kurume Institute of Technology, Japan

    Akifumi Makinouchi

Authors
  1. Yaokai Feng
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  2. Akifumi Makinouchi
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Editor information

Editors and Affiliations

  1. School of Computing, National University of Singapore,  

    Stéphane Bressan

  2. University of Linz, Altenbergerstraße 69, 4040, Linz, Austria

    Josef Küng  & Roland Wagner  & 

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© 2006 Springer-Verlag Berlin Heidelberg

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Feng, Y., Makinouchi, A. (2006). Efficient Evaluation of Partially-Dimensional Range Queries Using Adaptive R*-tree. In: Bressan, S., Küng, J., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2006. Lecture Notes in Computer Science, vol 4080. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11827405_67

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  • DOI: https://doi.org/10.1007/11827405_67

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-37871-6

  • Online ISBN: 978-3-540-37872-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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