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A Framework for Understanding Existing Databases

Abstract : In this paper, we propose a framework for a broad class of data mining algorithms for understanding existing databases: Functional and approximate dependency inference, minimal key inference, example relation generation and normal form tests. We point out that the common data centric step of these algorithms is the discovery of agree sets. A set-oriented approach for discovering agree sets from database relations based on SQL queries is proposed. Experiments have been performed in order to compare the proposed approach with a data mining approach. We present also a novel way to extract approximate functional dependencies having minimal errors from agree sets.
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Contributor : Stéphane Lopes <>
Submitted on : Tuesday, June 10, 2008 - 10:42:13 AM
Last modification on : Monday, January 20, 2020 - 12:12:05 PM


  • HAL Id : hal-00286627, version 1


Stéphane Lopes, Jean-Marc Petit, Lotfi Lakhal. A Framework for Understanding Existing Databases. International Database Engineering & Applications Symposium (IDEAS), 2001, Grenoble, France. pp.330-338. ⟨hal-00286627⟩



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