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Efficient Processing of Spatial Joins Using R-trees

Summary: First detailed study of spatial join processing using R*-trees at scale, addressing CPU and I/O bottlenecks. Techniques yield order-of-magnitude speedup over naive approaches; buffering makes I/O near-optimal, validated on large real datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
2644
Venue
SIGMOD
Year
1993
Pagerank
0.00041182766
Overall Rank
148 | 98.98%
DOI
-

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 9 of 59 citing papers.

Rank Citing Paper Year Venue Pagerank
9,017 Indexing Medium-dimensionality Data in Oracle 1999 SIGMOD 4.4091374e-05
9,864 Continuous Query Processing in Data Streams Using Duality of Data and Queries 2006 SIGMOD 4.2675822e-05
10,068 DiskJoin: Large-scale Vector Similarity Join with SSD 2026 SIGMOD 4.1945683e-05
10,507 SwiftSpatial: Spatial Joins on Modern Hardware 2025 SIGMOD 4.1945683e-05
10,621 BLAEQ: A Multigrid Index for Spatial Query on Geometry Data 2025 VLDB 4.1945683e-05
10,921 Optimal (Multiway) Spatial Joins 2024 PODS 4.1945683e-05
11,984 An Effective Encoding Scheme for Spatial RDF Data 2014 VLDB 4.1945683e-05
12,751 Algorithms for Querying by Spatial Structure 1998 VLDB 4.1945683e-05
14,114 GENESYS: A System for Efficient Spatial Query Processing 1994 SIGMOD -
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Outgoing Citations (Sorted by Pagerank)

Showing 8 of 8 cited papers.

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

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