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A GPU-friendly Geometric Data Model and Algebra for Spatial Queries

Summary: GPU-friendly geometric data model and algebra for spatial queries; data represented as geometric objects with composable GPU operators enabling broader GPU-accelerated spatial processing. Prototype supports a subset of operators, delivering orders-of-magnitude speedups over CPU and outperforming bespoke GPU approaches. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5990
Venue
SIGMOD
Year
2020
Pagerank
4.8845195e-05
Overall Rank
6,950 | 51.70%
DOI
10.1145/3318464.3389774

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Showing 4 of 4 cited papers.

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

Rank Cited Paper Year Venue Pagerank
721 Optimization Strategies for Spatial Query Processing 1991 VLDB 0.00017530857
2,194 How Good Are Modern Spatial Analytics Systems? 2018 VLDB 9.3268219e-05
5,390 IBM DB2 Spatial Extender - Spatial data within the RDBMS 2001 VLDB 5.5331855e-05
5,526 GPU Rasterization for Real-Time Spatial Aggregation over Arbitrary Polygons 2018 VLDB 5.4585713e-05
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