The "AI+R"-tree: An Instance-optimized R-tree

TL;DR

This paper introduces the AI+R-tree, a hybrid spatial index that leverages machine learning to optimize query performance, especially for high-overlap range queries, achieving up to 500% speedup over traditional R-trees.

Contribution

The paper proposes a novel hybrid AI+R-tree index that uses machine learning to distinguish query types and optimize search paths, improving spatial query efficiency.

Findings

Up to 500% performance improvement over traditional R-trees.

Effective differentiation between high- and low-overlap queries using learned models.

Significant reduction in extraneous leaf node accesses for high-overlap queries.

Abstract

The emerging class of instance-optimized systems has shown potential to achieve high performance by specializing to a specific data and query workloads. Particularly, Machine Learning (ML) techniques have been applied successfully to build various instance-optimized components (e.g., learned indexes). This paper investigates to leverage ML techniques to enhance the performance of spatial indexes, particularly the R-tree, for a given data and query workloads. As the areas covered by the R-tree index nodes overlap in space, upon searching for a specific point in space, multiple paths from root to leaf may potentially be explored. In the worst case, the entire R-tree could be searched. In this paper, we define and use the overlap ratio to quantify the degree of extraneous leaf node accesses required by a range query. The goal is to enhance the query performance of a traditional R-tree for high-overlap range queries as they tend to incur long running-times. We introduce a new AI-tree that transforms the search operation of an R-tree into a multi-label classification task to exclude the extraneous leaf node accesses. Then, we augment a traditional R-tree to the AI-tree to form a hybrid "AI+R"-tree. The "AI+R"-tree can automatically differentiate between the high- and low-overlap queries using a learned model. Thus, the "AI+R"-tree processes high-overlap queries using the AI-tree, and the low-overlap queries using the R-tree. Experiments on real datasets demonstrate that the "AI+R"-tree can enhance the query performance over a traditional R-tree by up to 500%.