Spatial Query Optimization With Learning
Summary: Tutorial/survey of learning-based approaches for spatial query optimization covering plan generation, cost estimation, query rewriting, and learning-driven spatial indexing/partitioning for both low- and high-dimensional geometries. Emphasizes adaptations needed for spatial semantics and pinpoints open problems such as scalability, model generalization across spatial datasets, index-aware learning, and handling geometric uncertainty. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xin Zhang (University of California Riverside)
- 2. Ahmed Eldawy (University of California Riverside)
BibTeX Citation
@article{zhang_vldb24,
title = {{Spatial Query Optimization With Learning}},
author = {Zhang, Xin and Eldawy, Ahmed},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {12},
pages = {4245--4248},
doi = {10.14778/3685800.3685846},
url = {https://doi.org/10.14778/3685800.3685846},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,095 | Are We There Yet? A Grand Tour of ETA Queries | 2026 | VLDB | 4.9605343e-05 |
| 11,482 | Large Language Models for Spatial Analysis Queries | 2025 | VLDB | 4.9605343e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 30 of 30 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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