  The Basic Counting problem [1] is one of the most fundamental and critical
streaming problems of sliding window queries over data streams. Given a stream
of 0's and 1's, the purpose of this problem is to estimate the number of 1's in
the last N elements (or time units) seen from the stream. Its solution can be
used as building blocks to solve numerous more complex problems such as heavy
hitter, frequency estimation, distinct counting, etc. In this paper, we present
the flattened exponential histogram (FEH) model for the Basic Counting problem.
Our model improves over the exponential histogram [1], [2], a well-received
deterministic technique for Basic Counting problem, with respect to accuracy
and memory utilization most of the time in practice. Extensive experimental
results on real-world datasets show that with the same memory footprint, the
accuracy of our model is between 4 to 15 and on average 7 times better than
that of the exponential histogram, while the speed is roughly the same.
