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.

中文翻译：

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PolyFit：用于快速近似范围聚合查询的基于多项式的索引方法

范围聚合查询在数据分析中找到了频繁的应用。在某些用例中，如果近似结果可以快速计算并满足近似保证，那么近似结果优于精确结果。受最近索引方法的启发，我们提供了通过连续函数表示离散点数据集的方法，然后可以用作紧凑的索引结构。更具体地说，我们开发了一种基于多项式的索引方法，称为 PolyFit，用于处理近似范围聚合查询。PolyFit 能够支持多种类型的范围聚合查询，包括 COUNT、SUM、MIN 和 MAX 聚合，并保证绝对和相对误差范围。实验结果表明，PolyFit 比现有的学习索引结构更快、更准确、更紧凑。