Abstract
This note studies the problem of estimating frequencies of items over data streams. We propose a simple streaming algorithm for the problem in small space complexity. Our algorithm is a counter-based algorithm with the aid of probabilistic counting. We show that our algorithm with k counters computes, with probability at least 1−δ, the estimation with relative error at most (1+ε)N/k, taking O(kloglogNk+klog(ε−1δ−1k)+klogℓ) space in expectation, where N is the total number of items and ℓ is the number of different items.
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