Comparing Synopsis Techniques for Approximate Spatial Data Analysis
Summary: Four spatial data synopsis techniques are compared across selectivity estimation, k-means clustering, and spatial partitioning to assess scalability for big spatial data. Extensive experiments on real and synthetic datasets up to 2.7B records compare synopsis vs baseline on generation time, query time, and accuracy, revealing when each method is most effective. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. A. B. Siddique (University of California Riverside)
- 2. Ahmed Eldawy (University of California Riverside)
- 3. Vagelis Hristidis (University of California Riverside)
BibTeX Citation
@article{siddique_vldb19,
title = {{Comparing Synopsis Techniques for Approximate Spatial Data Analysis}},
author = {Siddique, A. B. and Eldawy, Ahmed and Hristidis, Vagelis},
journal = {PVLDB},
series = {{VLDB} '19},
volume = {12},
number = {11},
pages = {1583--1596},
doi = {10.14778/3342263.3342635},
url = {https://doi.org/10.14778/3342263.3342635},
year = {2019}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,774 | The Case for Distance-Bounded Spatial Approximations | 2021 | CIDR | 5.2802274e-05 |
| 11,423 | Large Language Models for Spatial Analysis Queries | 2025 | VLDB | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 21 of 21 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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