RatioSketch: Towards More Accurate Frequency Estimation in Data Streams via a Lightweight Neural Network
Mengbo Wang, Zhuochen Fan, Dayu Wang, Guorui Xie, Qing Li, Zeyu Luan, Yong Jiang, Tong Yang
Abstract
Sketch-based solutions are widely used to estimate item frequencies in infinite data streams.Traditional hand-crafted sketches face the bottleneck of further eliminating errors because they cannot fully utilize the data stream distribution.Although recent neural sketches represented by MetaSketch and LegoSketch have improved generalization capabilities, they face bottlenecks such as high computational overhead and parameter sensitivity.Meanwhile, they ignore load information, fail to fully utilize the local information in hand-crafted sketches, and do not focus on the frequent items that are usually more important in data streams.In this paper, we propose RatioSketch, a novel lightweight neural network correction framework that synergizes the advantages of hand-crafted sketches and neural sketches in a ``micro-correction
BibTeX
@inproceedings{aaai2026_ratiosketchtowar,
title = {RatioSketch: Towards More Accurate Frequency Estimation in Data Streams via a Lightweight Neural Network},
author = {Mengbo Wang and Zhuochen Fan and Dayu Wang and Guorui Xie and Qing Li and Zeyu Luan and Yong Jiang and Tong Yang and Mingwei Xu},
booktitle = {AAAI 2026},
year = {2026}
}