Handling uncertainty in relational databases with possibility theory - A survey of different modelings

Olivier Pivert (1) , Henri Prade (2, 3)

1 IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires
2 IRIT-ADRIA - Argumentation, Décision, Raisonnement, Incertitude et Apprentissage
3 CNRS - Centre National de la Recherche Scientifique

Mainstream approaches to uncertainty modeling in relational databases are probabilistic. Still some researchers persist in proposing representations based on possibility theory. They are motivated by the ability of this latter setting for modeling epistemic uncertainty and by its qualitative nature. Interestingly enough, several possibilistic models have been proposed over time, and have been motivated by different application needs ranging from database querying, to database design and to data cleaning. Thus, one may distinguish between four different frameworks ordered here according to an increasing representation power: databases with (i) layered tuples; (ii) certainty-qualified attribute values; (iii) attribute values restricted by general possibility distributions; (iv) possibilistic c-tables. In each case, we discuss the role of the possibility-necessity duality, the limitations and the benefit of the representation settings, and their suitability with respect to different tasks.

Mots clés: Possibility theory; Relational databases; Uncertainty; Inconsistency; Data cleaning

Domaines: Intelligence artificielle [cs.AI]; Base de données [cs.DB]

Olivier Pivert, Henri Prade. Handling uncertainty in relational databases with possibility theory - A survey of different modelings. 12th International Conference on Scalable Uncertainty Management (SUM 2018), Oct 2018, Milan, Italy. pp.396-404, ⟨10.1007/978-3-030-00461-3_30⟩. ⟨hal-03634970⟩