Improving Tug-of-War sketch using Control-Variates method
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Computing space-efficient summary, or a.k.a. sketches, of large data, is a central problem in the streaming algorithm. Such sketches are used to answer post-hoc queries in several data analytics tasks. The algorithm for computing sketches typically requires to be fast, accurate, and space-efficient. A fundamental problem in the streaming algorithm framework is that of computing the frequency moments of data streams. The frequency moments of a sequence containing f_i elements of type i, are the numbers 𝐅_k=∑_i=1^n f_i^k, where i∈ [n]. This is also called as ℓ_k norm of the frequency vector (f_1, f_2, … f_n). Another important problem is to compute the similarity between two data streams by computing the inner product of the corresponding frequency vectors. The seminal work of Alon, Matias, and Szegedy <cit.>, a.k.a. Tug-of-war (or AMS) sketch gives a randomized sublinear space (and linear time) algorithm for computing the frequency moments, and the inner product between two frequency vectors corresponding to the data streams. However, the variance of these estimates typically tends to be large. In this work, we focus on minimizing the variance of these estimates. We use the techniques from the classical Control-Variate method <cit.> which is primarily known for variance reduction in Monte-Carlo simulations, and as a result, we are able to obtain significant variance reduction, at the cost of a little computational overhead. We present a theoretical analysis of our proposal and complement it with supporting experiments on synthetic as well as real-world datasets.
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