论文标题
Polyfit：基于多项式的索引方法，用于快速近似范围骨料查询
PolyFit: Polynomial-based Indexing Approach for Fast Approximate Range Aggregate Queries
论文作者
论文摘要
范围汇总查询在数据分析中经常应用。在某些用例中，如果可以快速计算并满足近似保证，则优于准确的结果，优于准确的结果。受到最新索引方法的启发，我们提供了代表连续函数设置的离散点数据的方法，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，用于处理近似范围的骨料查询。 PolyFit能够支持多种类型的范围聚合查询，包括计数，总和，最小和最大聚集体，并保证了绝对误差和相对误差界。实验结果表明，多菲特比现有学到的索引结构更快，更准确，紧凑。
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.
