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Dep-Miner: Un Algorithme d'Extraction des Dépendances Fonctionnelles

Abstract : 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 minimal 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 benchmark database. Experimental results show both the efficiency of our approach compared to the best current algorithm (i.e. Tane) described in a detailed way in this article, and the usefulness of real-world Armstrong relations.
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Contributor : Stéphane Lopes <>
Submitted on : Wednesday, April 9, 2008 - 2:35:25 PM
Last modification on : Monday, January 20, 2020 - 12:12:05 PM


  • HAL Id : hal-00271562, version 1


Stéphane Lopes, Jean-Marc Petit, Lotfi Lakhal. Dep-Miner: Un Algorithme d'Extraction des Dépendances Fonctionnelles. Revue des Sciences et Technologies de l'Information - Série TSI : Technique et Science Informatiques, Lavoisier, 2000, 19 (10), pp.1399-1428. ⟨hal-00271562⟩



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