An Overview on Querying and Learning in Temporal Probabilistic Databases

: 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.

• Type: Journal
• Year: 2015
• Where: BTW
• Authors: Maximilian Dylla
• Keywords: BTW 2015, Database