Abstract
Fuzzy set theory has proved to be a successful paradigm to extend the database relational model, augmenting its skill to capture uncertaintly. This capability may be consider in two levels: the data itself and the constraints defined to adjust the database schema to the real system. When constraints are considered, it is necessary to design methods to reason about it and not only a way to express them. This situation leads to a ambitious goal: the design of automated reasoning methods. Highly-expressive data models are not useful without an automated reasoning method. In this work we introduce an automated method to infer with fuzzy functional dependencies over a high level generalization of the relational model and provide its completeness result.
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Rodríguez-Jiménez, J.M., Cordero, P., Enciso, M., Mora, A. (2013). Automated Inference with Fuzzy Functional Dependencies over Graded Data. In: Rojas, I., Joya, G., Cabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7903. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38682-4_29
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