FastFDs: A Heuristic-Driven, Depth-First Algorithm for Mining Functional Dependencies from Relation Instances Extended Abstract

  • Catharine Wyss
  • Chris Giannella
  • Edward Robertson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2114)


The problem of discovering functional dependencies (FDs) from an existing relation instance has received considerable attention in the database research community. To date, even the most efficient solutions have exponential complexity in the number of attributes of the instance. We develop an algorithm, FastFDs, for solving this problem based on a depth-first, heuristic-driven (DFHD) search for finding minimal covers of hypergraphs. The technique of reducing the FD discovery problem to the problem of finding minimal covers of hypergraphs was applied previously by Lopes et al. in the algorithm Dep-Miner. Dep-Miner employs a levelwise search for minimal covers, whereas FastFDs uses DFHD search. We report several tests on distinct benchmark relation instances involving Dep-Miner, FastFDs, and Tane. Our experimental results indicate that DFHD search is more efficient than Dep-Miner’s levelwise search or Tane’s partitioning approach for many of these benchmark instances.


Functional Dependency Search Tree Correlation Factor Minimal Cover Relation Instance 
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 2001

Authors and Affiliations

  • Catharine Wyss
    • 1
  • Chris Giannella
    • 1
  • Edward Robertson
    • 1
  1. 1.Computer Science DepartmentIndiana UniversityBloomingtonUSA

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