Learning-Augmented Frequency Estimation in Sliding Windows

TL;DR

This paper introduces machine learning-enhanced algorithms for approximate frequency estimation in sliding windows, significantly improving memory-accuracy tradeoffs by predicting item reappearances and filtering stream data.

Contribution

It presents novel methods leveraging predictions to enhance sliding window frequency estimation, addressing challenges of dynamic data streams and demonstrating practical improvements.

Findings

Theoretical bounds show improved memory-accuracy tradeoffs.

Experimental results confirm effectiveness on real-world datasets.

Predictors help filter items with large next arrival times, boosting performance.

Abstract

We show how to utilize machine learning approaches to improve sliding window algorithms for approximate frequency estimation problems, under the ``algorithms with predictions'' framework. In this dynamic environment, previous learning-augmented algorithms are less effective, since properties in sliding window resolution can differ significantly from the properties of the entire stream. Our focus is on the benefits of predicting and filtering out items with large next arrival times -- that is, there is a large gap until their next appearance -- from the stream, which we show improves the memory-accuracy tradeoffs significantly. We provide theorems that provide insight into how and by how much our technique can improve the sliding window algorithm, as well as experimental results using real-world data sets. Our work demonstrates that predictors can be useful in the challenging sliding window setting.