Online aggregation processing in relational database systems enables users to both observe the progress of their aggregation queries and control execution of these queries on the fly. Running confidence intervals are an important component of an online aggregation system and indicate to the user the proximity of each running aggregate to the corresponding final result. Large-sample confidence intervals contain the final query result with a prespecified probability and rest on central limit theorems, while deterministic confidence intervals contain the final query result with probability 1. We provide formulas that can be used to compute both large-sample and deterministic confidence intervals for a variety of aggregates encountered in practice. The formulas are applicable to single-table AVG, COUNT, SUM, VARIANCE, and STD DEV queries with a selection predicate, and to multi-table AVG, SUM, and COUNT queries with joint and selection predicates.
By: Peter J. Haas
Published in: RJ10050 in 1996
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