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SketchPolymer: Estimate Per-item Tail Quantile Using One Sketch

Jiarui Guo, Yisen Hong, Yuhan Wu, Yunfei Liu, Tong Yang, Bin Cui

2023Year
13Citations
5Top-tier citations

Abstract

1 Estimating the quantile of distribution, especially tail distribution, is an interesting topic in data stream models, and has obtained extensive interest from many researchers. In this paper, we propose a novel sketch, namely SketchPolymer to accurately estimate per-item tail quantile. SketchPolymer uses a technique called Early Filtration to filter infrequent items, and another technique called VSS to reduce error. Our experimental results show that the accuracy of SketchPolymer is on average 32.67 times better than state-of-the-art techniques. We also implement our SketchPolymer on P4 and FPGA platforms to verify its deployment flexibility. All our codes are available at GitHub [1].

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