Semantics and evaluation of top-k queries in probabilistic databases

Abstract

We study here fundamental issues involved in top-k query evaluation in probabilistic databases. We consider simple probabilistic databases in which probabilities are associated with individual tuples, and general probabilistic databases in which, additionally, exclusivity relationships between tuples can be represented. In contrast to other recent research in this area, we do not limit ourselves to injective scoring functions. We formulate three intuitive postulates for the semantics of top-k queries in probabilistic databases, and introduce a new semantics, Global-Topk, that satisfies those postulates to a large degree. We also show how to evaluate queries under the Global-Topk semantics. For simple databases we design dynamic-programming based algorithms. For general databases we show polynomial-time reductions to the simple cases, and provide effective heuristics to speed up the computation in practice. For example, we demonstrate that for a fixed k the time complexity of top-k query evaluation is as low as linear, under the assumption that probabilistic databases are simple and scoring functions are injective.