  As of 2005, sampling has been incorporated in all major database systems.
While efficient sampling techniques are realizable, determining the accuracy of
an estimate obtained from the sample is still an unresolved problem. In this
paper, we present a theoretical framework that allows an elegant treatment of
the problem. We base our work on generalized uniform sampling (GUS), a class of
sampling methods that subsumes a wide variety of sampling techniques. We
introduce a key notion of equivalence that allows GUS sampling operators to
commute with selection and join, and derivation of confidence intervals. We
illustrate the theory through extensive examples and give indications on how to
use it to provide meaningful estimations in database systems.
