Samples for Understanding Data-Semantics in Relations

  • Fabien De Marchi
  • Stéphane Lopes
  • Jean-Marc Petit
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2366)

Abstract

From statistics, sampling technics were proposed and some of them were proved to be very useful in many database applications. Rather surprisingly, it seems these works never consider the preservation of data semantics. Since functional dependencies (FDs) are known to convey most of data semantics, an interesting issue would be to construct samples preserving FDs satisfied in existing relations.

To cope with this issue, we propose in this paper to define Informative Armstrong Relations (IARs); a relation s is an IAR for a relation r if s is a subset of r and if FDs satisfied in s are exactly the same as FDs satisfied in r. Such a relation always exists since r is obviously an IAR for itself; moreover we shall point out that small IARs with interesting bounded sizes exist. Experiments on relations available in the KDD archive were conducted and highlight the interest of IARs to sample existing relations.

Keywords

Functional Dependency Horn Clause Data Semantic Initial Relation Extend Database Technology 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Fabien De Marchi
    • 1
  • Stéphane Lopes
    • 2
  • Jean-Marc Petit
    • 1
  1. 1.Laboratoire LIMOS, CNRS UMR 2239Université Blaise Pascal - Clermont-Ferrand IIAubière cedexFrance
  2. 2.Laboratoire PRISMCNRS FRE 8636Versailles CedexFrance

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