Continuous Uncertainty in Trio

Abstract

We present extensions to Trio for incorporating continuous uncertainty into the system. Data items with uncertain possible values drawn from a continuous domain are represented through a generic set of functions. Our approach enables precise and efficient representation of arbitrary probability distribution functions, along with standard distributions such as Gaussians. We also describe how queries are processed efficiently over this representation, without knowledge of specific distributions. For queries that cannot be answered exactly, we can provide approximate answers using sampling or histogram approximations, offering the user a cost-precision trade-off. Our approach exploits Trio’s lineage and confidence features, with smooth integration into the overall data model and system.