Incremental Partitioning for Efficient Spatial Data Analytics
Summary: Incrementally maintains block-level spatial partitions under bulk inserts and deletes in distributed file systems, bridging scalable analytics with update support. Formalizes the NP-hard problem, introduces a query-aware cost model, and delivers three outperforming implementations. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Tin Vu (University of California Riverside)
- 2. Ahmed Eldawy (University of California Riverside)
- 3. Vagelis Hristidis (University of California Riverside)
- 4. Vassilis Tsotras (University of California Riverside)
BibTeX Citation
@article{vu_vldb22,
title = {{Incremental Partitioning for Efficient Spatial Data Analytics}},
author = {Vu, Tin and Eldawy, Ahmed and Hristidis, Vagelis and Tsotras, Vassilis},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {3},
pages = {713--726},
doi = {10.14778/3494124.3494150},
url = {https://doi.org/10.14778/3494124.3494150},
year = {2022}
}
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,896 | Aster: Enhancing LSM-structures for Scalable Graph Database | 2025 | SIGMOD | 5.3495662e-05 |
| 9,021 | Structural Designs Meet Optimality: Exploring Optimized LSM-tree Structures in A Colossal Configuration Space | 2024 | SIGMOD | 5.3305499e-05 |
| 11,392 | ST4ML: Machine Learning Oriented Spatio-Temporal Data Processing at Scale | 2023 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 19 of 19 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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