Making Aggregation Work in Uncertain and Probabilistic Databases

Abstract

We describe how aggregation is handled in the \emph{Trio} system for uncertain and probabilistic data. Because ``exact'' aggregation in uncertain databases can produce exponentially-sized results, we provide three alternatives: a {\em low} bound on the aggregate value, a {\em high} bound on the value, and the {\em expected} value. These variants return a single result instead of a set of possible results, and they are generally efficient to compute for both full-table and grouped aggregation queries. We provide formal definitions and semantics and a description of our implementation for single-table aggregation queries. We study the performance and scalability of our algorithms through experiments over a large synthetic data set. We also provide some preliminary results on aggregations over joins.