PolyFit: Polynomial-based Indexing Approach for Fast Approximate Range Aggregate Queries

TL;DR

PolyFit introduces a polynomial-based indexing method that enables fast, approximate range aggregate queries with guaranteed error bounds, outperforming existing learned index structures in speed, accuracy, and compactness.

Contribution

The paper presents PolyFit, a novel polynomial-based index structure that efficiently supports multiple approximate range aggregate queries with error guarantees.

Findings

PolyFit is faster than existing learned index structures.

PolyFit provides more accurate approximate query results.

PolyFit produces more compact index structures.

Abstract

Range aggregate queries find frequent application in data analytics. In some use cases, approximate results are preferred over accurate results if they can be computed rapidly and satisfy approximation guarantees. Inspired by a recent indexing approach, we provide means of representing a discrete point data set by continuous functions that can then serve as compact index structures. More specifically, we develop a polynomial-based indexing approach, called PolyFit, for processing approximate range aggregate queries. PolyFit is capable of supporting multiple types of range aggregate queries, including COUNT, SUM, MIN and MAX aggregates, with guaranteed absolute and relative error bounds. Experiment results show that PolyFit is faster and more accurate and compact than existing learned index structures.