Logical foundations of possibilistic keys

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Journal Article
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, 8761 pp. 181 - 195
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© Springer International Publishing Switzerland 2014. Possibility theory is applied to introduce and reason about the fundamental notion of a key for uncertain data. Uncertainty is modeled qualitatively by assigning to tuples of data a degree of possibility with which they occur in a relation, and assigning to keys a degree of certainty which says to which tuples the key applies. The associated implication problem is characterized axiomatically and algorithmically. It is shown how sets of possibilistic keys can be visualized as possibilistic Armstrong relations, and how they can be discovered from given possibilistic relations. It is also shown how possibilistic keys can be used to clean dirty data by revising the belief in possibility degrees of tuples.
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