This paper considers the problem of maintaining statistic aggregates over the
last W elements of a data stream. First, the problem of counting the number of
1's in the last W bits of a binary stream is considered. A lower bound of
{\Omega}(1/{\epsilon} + log W) memory bits for W{\epsilon}-additive
approximations is derived. This is followed by an algorithm whose memory
consumption is O(1/{\epsilon} + log W) bits, indicating that the algorithm is
optimal and that the bound is tight. Next, the more general problem of
maintaining a sum of the last W integers, each in the range of {0,1,...,R}, is
addressed. The paper shows that approximating the sum within an additive error
of RW{\epsilon} can also be done using {\Theta}(1/{\epsilon} + log W) bits for
{\epsilon}={\Omega}(1/W). For {\epsilon}=o(1/W), we present a succinct
algorithm which uses B(1 + o(1)) bits, where B={\Theta}(Wlog(1/W{\epsilon})) is
the derived lower bound. We show that all lower bounds generalize to randomized
algorithms as well. All algorithms process new elements and answer queries in
O(1) worst-case time.Comment: A shorter version appears in SWAT 201
