  The efficient estimation of frequency moments of a data stream in one-pass
using limited space and time per item is one of the most fundamental problem in
data stream processing. An especially important estimation is to find the
number of distinct elements in a data stream, which is generally referred to as
the zeroth frequency moment and denoted by $F_0$. In this paper, we consider
streams of rectangles defined over a discrete space and the task is to compute
the total number of distinct points covered by the rectangles. This is known as
the Klee's measure problem in 2 dimensions. We present and analyze a randomized
streaming approximation algorithm which gives an $(\epsilon,
\delta)$-approximation of $F_0$ for the total area of Klee's measure problem in
2 dimensions. Our algorithm achieves the following complexity bounds: (a) the
amortized processing time per rectangle is $O(\frac{1}{\epsilon^4}\log^3
n\log\frac{1}{\delta})$; (b) the space complexity is
$O(\frac{1}{\epsilon^2}\log n \log\frac{1}{\delta})$ bits; and (c) the time to
answer a query for $F_0$ is $O(\log\frac{1}{\delta})$, respectively. To our
knowledge, this is the first streaming approximation for the Klee's measure
problem that achieves sub-polynomial bounds.
