An overview on querying and learning in temporal probabilistic databases

Abstract

Probabilistic databases store, query and manage large amounts of uncertain information in an efficient way. This paper summarizes my thesis which advances the state-of-the-art in probabilistic databases in three different ways: First, we present a closed and complete data model for temporal probabilistic databases. Queries are posed via temporal deduction rules which induce lineage formulas capturing both time and uncertainty. Second, we devise a methodology for computing the top-k most probable query answers. It is based on first-order lineage formulas representing sets of answer candidates. Moreover, we derive probability bounds on these formulas which enable pruning low-probability answers. Third, we introduce the problem of learning tuple probabilities, which allows updating and cleaning of probabilistic databases, and study its complexity and characterize its solutions.