A Reinforcement Learning Based R-Tree for Spatial Data Indexing in Dynamic Environments

TL;DR

This paper introduces a reinforcement learning approach to enhance R-Tree spatial index performance in dynamic environments without altering its existing structure or query algorithms.

Contribution

It proposes using reinforcement learning to optimize subtree selection and node splitting in R-Trees, avoiding the need to replace the entire index structure.

Findings

RL-based R-Tree outperforms traditional R-Tree in query time

Effective on datasets with over 100 million objects

Improves index performance without structural changes

Abstract

Learned indices have been proposed to replace classic index structures like B-Tree with machine learning (ML) models. They require to replace both the indices and query processing algorithms currently deployed by the databases, and such a radical departure is likely to encounter challenges and obstacles. In contrast, we propose a fundamentally different way of using ML techniques to improve on the query performance of the classic R-Tree without the need of changing its structure or query processing algorithms. Specifically, we develop reinforcement learning (RL) based models to decide how to choose a subtree for insertion and how to split a node when building an R-Tree, instead of relying on hand-crafted heuristic rules currently used by R-Tree and its variants. Experiments on real and synthetic datasets with up to more than 100 million spatial objects clearly show that our RL based index outperforms R-Tree and its variants in terms of query processing time.