Approximate dependency inference from relations

  • Jyrki Kivinen
  • Heikki Mannila
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 646)


The functional dependency inference problem consists of finding a cover for the set dep(r) of functional dependencies that hold in a given relation r. All known algorithms for this task have running times that can be in the worst case exponential in the size of the smallest cover of the dependency set. We consider approximate dependency inference. We define various measures for the error of a dependency in a relation. These error measures have the value 0 if the dependency holds and a value close to 1 if the dependency clearly does not hold. Depending on the measure used, all dependencies with error at least ɛ in r can be detected with high probability by considering only O(1/ɛ) or O(¦r¦1/2/ɛ) random tuples of r. We also show how a machine learning algorithm due to Angluin, Frazier and Pitt can be applied to give in output-polynomial time an approximately correct cover for the set of dependencies holding in r.


Polynomial Time Functional Dependency Horn Clause Query Optimization Small Cover 
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 1992

Authors and Affiliations

  • Jyrki Kivinen
    • 1
  • Heikki Mannila
    • 1
  1. 1.Department of Computer ScienceUniversity of HelsinkiHelsinkiFinland

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