RatioSketch: Towards More Accurate Frequency Estimation in Data Streams via a Lightweight Neural Network

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 handcrafted 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 "microcorrection" paradigm. The key idea is to retain the efficient underlying data structure of the hand-crafted sketch and to build a neural correction layer in its output space. We select multiple representative hand-crafted sketches as use cases to study the correction performance of RatioSketch on them. Extensive experimental evaluations on several real-world datasets show that RatioSketch-corrected sketches achieve consistently higher estimation accuracy than their uncorrected counterparts, as well as outperforming neural baselines such as MetaSketch and LegoSketch under identical memory budgets.