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CoDS: A Representative Sampling Method for Relational Databases

  • Teodora Sandra Buda
  • Thomas Cerqueus
  • John Murphy
  • Morten Kristiansen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8055)

Abstract

Database sampling has become a popular approach to handle large amounts of data in a wide range of application areas such as data mining or approximate query evaluation. Using database samples is a potential solution when using the entire database is not cost-effective, and a balance between the accuracy of the results and the computational cost of the process applied on the large data set is preferred. Existing sampling approaches are either limited to specific application areas, to single table databases, or to random sampling. In this paper, we propose CoDS: a novel sampling approach targeting relational databases that ensures that the sample database follows the same distribution for specific fields as the original database. In particular it aims to maintain the distribution between tables. We evaluate the performance of our algorithm by measuring the representativeness of the sample with respect to the original database. We compare our approach with two existing solutions, and we show that our method performs faster and produces better results in terms of representativeness.

Keywords

Relational database Representative database sampling 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Teodora Sandra Buda
    • 1
  • Thomas Cerqueus
    • 1
  • John Murphy
    • 1
  • Morten Kristiansen
    • 2
  1. 1.Lero, Performance Engineering Lab School of Computer Science and InformaticsUniversity College DublinIreland
  2. 2.IBM Software GroupIBM Collaboration SolutionsDublinIreland

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