Abstract
In the context of the data quality research area, Conditional Functional Dependencies with built-in predicates (CFDps) have been recently defined as extensions of Conditional Functional Dependencies with the addition, in the patterns of their data values, of the comparison operators. CFDps can be used to impose constraints on data; they can also represent relationships among data, and therefore they can be mined from datasets. In the present work, after having introduced the distinction between constant and non-constant CFDps, we describe an algorithm to discover non-constant CFDps from datasets.
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Zanzi, A., Trombetta, A. (2014). Discovering non-constant Conditional Functional Dependencies with Built-in Predicates. In: Decker, H., Lhotská, L., Link, S., Spies, M., Wagner, R.R. (eds) Database and Expert Systems Applications. DEXA 2014. Lecture Notes in Computer Science, vol 8644. Springer, Cham. https://doi.org/10.1007/978-3-319-10073-9_4
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DOI: https://doi.org/10.1007/978-3-319-10073-9_4
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