Hierarchically organized skew-tolerant histograms for geographic data objects

Yohan J. Roh, Jae H. Kim, Yon Dohn Chung, Jin Hyun Son, Myoung H. Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Citations (Scopus)

Abstract

Histograms have been widely used for fast estimation of query result sizes in query optimization. In this paper, we propose a new histogram method, called the Skew-Tolerant Histogram (STHistogram) for two or three dimensional geographic data objects that are used in many real-world applications in practice. The proposed method provides a significantly enhanced accuracy in a robust manner even for the data set that has a highly skewed distribution. Our method detects hotspots present in various parts of a data set and exploits them in organizing histogram buckets. For this purpose, we first define the concept of a hotspot, and provide an algorithm that efficiently extracts hotspots from the given data set. Then, we present our histogram construction method that utilizes hotspot information. We also describe how to estimate query result sizes by using the proposed histogram. We show through extensive performance experiments that the proposed method provides better performance than other existing methods.

Original languageEnglish
Title of host publicationProceedings of the ACM SIGMOD International Conference on Management of Data
Pages627-638
Number of pages12
DOIs
Publication statusPublished - 2010 Jul 23
Event2010 International Conference on Management of Data, SIGMOD '10 - Indianapolis, IN, United States
Duration: 2010 Jun 62010 Jun 11

Other

Other2010 International Conference on Management of Data, SIGMOD '10
CountryUnited States
CityIndianapolis, IN
Period10/6/610/6/11

Keywords

  • histograms
  • query optimization
  • spatial databases

ASJC Scopus subject areas

  • Information Systems
  • Software

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  • Cite this

    Roh, Y. J., Kim, J. H., Chung, Y. D., Son, J. H., & Kim, M. H. (2010). Hierarchically organized skew-tolerant histograms for geographic data objects. In Proceedings of the ACM SIGMOD International Conference on Management of Data (pp. 627-638) https://doi.org/10.1145/1807167.1807236