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Random Databases with Correlated Data

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 7260)

Abstract

A model of random databases is given, with arbitrary correlations among the data of one individual. This is given by a joint distribution function. The individuals are chosen independently, their number m is considered to be (approximately) known. The probability of the event that a given functional dependency A → b holds (A is a set of attributes, b is an attribute) is determined in a limiting sense. This probability is small if m is much larger than \(2^{H_2(A\rightarrow b)/2}\) and is large if m is much smaller than \(2^{H_2(A\rightarrow b)/2}\) where H 2(A → b) is an entropy like functional of the probability distribution of the data.

Keywords

  • Random Vector
  • Functional Dependency
  • Statistical Dependence
  • Correlate Data
  • Apply Probability

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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References

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Katona, G.O.H. (2012). Random Databases with Correlated Data. In: Düsterhöft, A., Klettke, M., Schewe, KD. (eds) Conceptual Modelling and Its Theoretical Foundations. Lecture Notes in Computer Science, vol 7260. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28279-9_4

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  • DOI: https://doi.org/10.1007/978-3-642-28279-9_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28278-2

  • Online ISBN: 978-3-642-28279-9

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