Probabilistic Cardinality Constraints

Abstract

Probabilistic databases address well the requirements of an increasing number of modern applications that produce large collections of uncertain data. We propose probabilistic cardinality constraints as a principled tool for controlling the occurrences of data patterns in probabilistic databases. Our constraints help organizations balance their targets for different data quality dimensions, and infer probabilities on the number of query answers. These applications are unlocked by developing algorithms to reason efficiently about probabilistic cardinality constraints, and to help analysts acquire the marginal probability by which cardinality constraints hold in a given application domain. For this purpose, we overcome technical challenges to compute Armstrong PC-sketches as succinct data samples that perfectly visualize any given perceptions about these marginal probabilities.