Revisiting the formal foundation of Probabilistic Databases

Abstract

One of the core problems in soft computing is dealing with uncertainty in data. In this paper, we revisit the formal foundation of a class of probabilistic databases with the purpose to (1) obtain data model independence, (2) separate metadata on uncertainty and probabilities from the raw data, (3) better understand aggregation, and (4) create more opportunities for optimization. The paper presents the formal framework and validates data model independence by showing how to a obtain probabilistic Datalog as well as a probabilistic relational algebra by applying the framework to their non-probabilistic counterparts. We conclude with a discussion on the latter three goals.