NeuroSketch: Fast and Approximate Evaluation of Range Aggregate Queries with Neural Networks

TL;DR

NeuroSketch introduces a neural network framework that models queries rather than data, providing theoretical error bounds and achieving significantly faster and more accurate approximate range aggregate query answers in practice.

Contribution

The paper shifts focus to modeling queries instead of data, enabling theoretical analysis and improved performance in neural network-based RAQ answering.

Findings

NeuroSketch answers RAQs orders of magnitude faster than existing methods.

It achieves better accuracy in approximate query answering.

Theoretical error bounds are validated through extensive experiments.

Abstract

Range aggregate queries (RAQs) are an integral part of many real-world applications, where, often, fast and approximate answers for the queries are desired. Recent work has studied answering RAQs using machine learning (ML) models, where a model of the data is learned to answer the queries. However, there is no theoretical understanding of why and when the ML based approaches perform well. Furthermore, since the ML approaches model the data, they fail to capitalize on any query specific information to improve performance in practice. In this paper, we focus on modeling ``queries'' rather than data and train neural networks to learn the query answers. This change of focus allows us to theoretically study our ML approach to provide a distribution and query dependent error bound for neural networks when answering RAQs. We confirm our theoretical results by developing NeuroSketch, a neural network framework to answer RAQs in practice. Extensive experimental study on real-world, TPC-benchmark and synthetic datasets show that NeuroSketch answers RAQs multiple orders of magnitude faster than state-of-the-art and with better accuracy.