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Performance of Data-Parallel Spatial Operations

Summary: Data-parallel spatial ops (build, polygonization, spatial join) for planar line segments compare bucket PMR quadtree, R-tree, and R+-tree on the Connection Machine. Bucket PMR quadtree outperforms the others due to a regular disjoint space decomposition that boosts parallelism and reduces interprocessor communication. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
8380
Venue
VLDB
Year
1994
Pagerank
5.9227722e-05
Overall Rank
6,298 | 56.80%
DOI
-

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{hoelt_vldb94,
        title = {{Performance of Data-Parallel Spatial Operations}},
        author = {Hoelt, Erik G. and Samet, Hanan},
        journal = {PVLDB},
        series = {{VLDB} '94},
        pages = {156},
        year = {1994}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
4,148 Spatial Partitioning Techniques in SpatialHadoop 2015 VLDB 6.872045e-05
5,795 Benchmarking Spatial Join Operations with Spatial Output 1995 VLDB 6.0859946e-05
8,644 Incremental Partitioning for Efficient Spatial Data Analytics 2022 VLDB 5.3934068e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 10 of 10 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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