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