Statistical analysis of sketch estimators

Sketching techniques can provide approximate answers to aggregate queries either for data-streaming or distributed computation. Small space summaries that have linearity properties are required for both types of applications. The prevalent method for analyzing sketches uses moment analysis and distribution independent bounds based on moments. This method produces clean, easy to interpret, theoretical bounds that are especially useful for deriving asymptotic results. However, the theoretical bounds obscure fine details of the behavior of various sketches and they are mostly not indicative of which type of sketches should be used in practice. Moreover, no significant empirical comparison between various sketching techniques has been published, which makes the choice even harder. In this paper, we take a close look at the sketching techniques proposed in the literature from a statistical point of view with the goal of determining properties that indicate the actual behavior and producing tighter confidence bounds. Interestingly, the statistical analysis reveals that two of the techniques, Fast-AGMS and Count-Min, provide results that are in some cases orders of magnitude better than the corresponding theoretical predictions. We conduct an extensive empirical study that compares the different sketching techniques in order to corroborate the statistical analysis with the conclusions we draw from it. The study indicates the expected performance of various sketches, which is crucial if the techniques are to be used by practitioners. The overall conclusion of the study is that Fast-AGMS sketches are, for the full spectrum of problems, either the best, or close to the best, sketching technique. This makes Fast-AGMS sketches the preferred choice irrespective of the situation.