Detecting ambiguity in prioritized database repairing

In its traditional definition, a repair of an inconsistent database is a consistent database that differs from the inconsistent one in a "minimal way." Often, repairs are not equally legitimate, as it is desired to prefer one over another; for example, one fact is regarded more reliable than another, or a more recent fact should be preferred to an earlier one. Motivated by these considerations, researchers have introduced and investigated the framework of preferred repairs, in the context of denial constraints and subset repairs. There, a priority relation between facts is lifted towards a priority relation between consistent databases, and repairs are restricted to the ones that are optimal in the lifted sense. Three notions of lifting (and optimal repairs) have been proposed: Pareto, global, and completion. In this paper we investigate the complexity of deciding whether the priority relation suffices to clean the database unambiguously, or in other words, whether there is exactly one optimal repair. We show that the different lifting semantics entail highly different complexities. Under Pareto optimality, the problem is coNP-complete, in data complexity, for every set of functional dependencies (FDs), except for the tractable case of (equivalence to) one FD per relation. Under global optimality, one FD per relation is still tractable, but we establish Π^p₂-completeness for a relation with two FDs. In contrast, under completion optimality the problem is solvable in polynomial time for every set of FDs. In fact, we present a polynomial-time algorithm for arbitrary conflict hypergraphs. We further show that under a general assumption of transitivity, this algorithm solves the problem even for global optimality. The algorithm is extremely simple, but its proof of correctness is quite intricate.