Efficient Discovery of Functional Dependencies and Armstrong Relations

  • Stéphane Lopes
  • Jean-Marc Petit
  • Lotfi Lakhal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1777)


In this paper, we propose a new efficient algorithm called Dep-Miner for discovering minimal non-trivial functional dependencies from large databases. Based on theoretical foundations, our approach combines the discovery of functional dependencies along with the construction of real-world Armstrong relations (without additional execution time). These relations are small Armstrong relations taking their values in the initial relation. Discovering both minimal functional dependencies and real-world Armstrong relations facilitate the tasks of database administrators when maintaining and analyzing existing databases. We evaluate Dep-Miner performances by using a new benchmark database. Experimental results show both the efficiency of our approach compared to the best current algorithm (i.e. Tane), and the usefulness of real-world Armstrong relations.


Equivalence Class Association Rule Relational Database Functional Dependency Benchmark Database 
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 2000

Authors and Affiliations

  • Stéphane Lopes
    • 1
  • Jean-Marc Petit
    • 1
  • Lotfi Lakhal
    • 1
  1. 1.Laboratoire LIMOSUniversité Blaise Pascal - Clermont-Ferrand II Campus Universitaire des CézeauxAubière cedexFrance

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