RL-RTree: A Reinforcement Learning-Optimized Dynamic R-Tree for High-Dimensional Spatial Indexing

Spatial indexing in high-dimensional dynamic environments faces critical challenges, including the curse of dimensionality and rapid distribution shifts, which degrade the performance of traditional indexes like R*-trees and static learned indexes. We propose RL-RTree, a dynamic R-tree op...

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Bibliographic Details
Main Author: Yongxin Peng
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/11053811/
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