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On Linear-Spline Based Histograms

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Advances in Web-Age Information Management (WAIM 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2419))

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Abstract

Approximation is a very effective paradigm to speed up query processing in large databases. One popular approximation mechanism is data size reduction. There are three reduction techniques: sampling, histograms, and wavelets. Histogram techniques are supported by many commercial database systems, and have been shown very effective for approximately processing aggregation queries. In this paper, we will investigate the optimal models for building histograms based on linear spline techniques. We will firstly propose several novel models. Secondly, we will present efficient algorithms to achieve these proposed optimal models. Our experiment results showed that our new techniques can greatly improve the approximation accuracy comparing to the existing techniques.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, 2052, Australia

    Qing Zhang & Xuemin Lin

Authors
  1. Qing Zhang
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  2. Xuemin Lin
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Editor information

Editors and Affiliations

  1. Information School, Renmin University of China, Beijing, 100872, China

    Xiaofeng Meng

  2. Department of Computer Science, University of California, Santa Barbara, CA, 93106-5110, USA

    Jianwen Su  & Yujun Wang  & 

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© 2002 Springer-Verlag Berlin Heidelberg

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Zhang, Q., Lin, X. (2002). On Linear-Spline Based Histograms. In: Meng, X., Su, J., Wang, Y. (eds) Advances in Web-Age Information Management. WAIM 2002. Lecture Notes in Computer Science, vol 2419. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45703-8_33

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  • DOI: https://doi.org/10.1007/3-540-45703-8_33

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44045-1

  • Online ISBN: 978-3-540-45703-9

  • eBook Packages: Springer Book Archive

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