Sequential sampling algorithms have recently attracted interest
as a way to design scalable algorithms for Data mining and KDD processes.
In this paper,
we identify an elementary sequential sampling task
(estimation from examples),
from which one can derive many other tasks appearing in practice.
We present a generic algorithm
to solve this task and an analysis of its correctness and running time
that is simpler and more intuitive
than those existing in the literature.
For two specific tasks,
frequency and advantage estimation,
we derive lower bounds on running time in addition
to the general upper bounds.