Effectively Learning Spatial Indices
Summary: Proposes a learned index for 2D spatial data using rank-space ordering to form an indexable sequence and block-wise learning for scalability. A recursive partitioning strategy, with experiments on 100M+ points, yields query speedups of over an order of magnitude versus R-trees and prior learned indexes. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jianzhong Qi (University of Melbourne)
- 2. Guanli Liu (University of Melbourne)
- 3. Christian S. Jensen (Aalborg University)
- 4. Lars Kulik (University of Melbourne)
BibTeX Citation
@article{qi_vldb20,
title = {{Effectively Learning Spatial Indices}},
author = {Qi, Jianzhong and Liu, Guanli and Jensen, Christian S. and Kulik, Lars},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {11},
pages = {2341--2354},
doi = {10.14778/3407790.3407829},
url = {https://doi.org/10.14778/3407790.3407829},
year = {2020}
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