A Streaming Approximation Algorithm for Klee's Measure Problem

Abstract: 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.