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Denial constraints (DCs) express rules that identify inconsistencies in a database. Their design, however, is an onerous task. We propose a method that discovers DCs based on evidence of the tuples of a dataset. Our method discovers reliable DCs, even if the dataset has errors. Our experiments with real data show that it is possible to find DCs that, with high precision and recall, point to inconsistencies of the input data.

Short Paper: Descoberta automática de restrições de negação confiáveis