WavingSketch: an unbiased and generic sketch for finding top-k items in data streams

Finding top-k items in data streams is a fundamental problem in data mining. Unbiased estimation is well acknowledged as an elegant and important property for top-k algorithms. In this paper, we propose a novel sketch algorithm, called WavingSketch, which is more accurate than existing unbiased algorithms. We theoretically prove that WavingSketchcan provide unbiased estimation, and derive its error bound. WavingSketchis generic to measurement tasks, and we apply it to five applications: finding top-k frequent items, finding top-k heavy changes, finding top-k persistent items, finding top-k Super-Spreaders, and join-aggregate estimation. Our experimental results show that, compared with the state-of-the-art Unbiased Space-Saving, WavingSketchachieves \(10 \times \) $10\times$ faster speed and \(10^3 \times \) ${10}^3\times$ smaller error on finding frequent items. For other applications, WavingSketchalso achieves higher accuracy and faster speed. All related codes are open-sourced at GitHub (https://github.com/WavingSketch/Waving-Sketch).