A framework for understanding existing databases

The authors 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 also present a novel way to extract approximate functional dependencies having minimal errors from agree sets.

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