Spatial Hash-Joins
Summary: Defines a spatial hash-join framework with a two-component partition: bucket extents plus an assignment function; inner partitions adapt while outer partitions are immutable and may replicate items into multiple buckets. No precomputed indices required; it outperforms tree-based spatial joins (even with indices) and offers predictable costs and easy integration with existing hash-join engines. (summarized by gpt-5-nano on Feb 09 2026)
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| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,016 | Memory-Efficient Hash Joins | 2015 | VLDB | 0.00014638492 |
| 540 | Design and Evaluation of Main Memory Hash Join Algorithms for Multi-core CPUs | 2011 | SIGMOD | 0.0002063443 |
| 1,856 | An Adaptive Hash Join Algorithm for Multiuser Environments | 1990 | VLDB | 0.00010304993 |
| 5,322 | Generalized Hash Teams for Join and Group-by | 1999 | VLDB | 5.5701077e-05 |
| 2,672 | Spatial Joins Using Seeded Trees | 1994 | SIGMOD | 8.3363695e-05 |
| 4,217 | Spatial Partitioning Techniques in SpatialHadoop | 2015 | VLDB | 6.3514771e-05 |
| 10,921 | Optimal (Multiway) Spatial Joins | 2024 | PODS | 4.1945683e-05 |
| 6,454 | On Spatially Partitioned Temporal Join | 1994 | VLDB | 5.0569697e-05 |
| 4,529 | Integration of Spatial Join Algorithms for Processing Multiple Inputs | 1999 | SIGMOD | 6.1078661e-05 |
| 550 | Hash-Partitioned Join Method Using Dynamic Destaging Strategy | 1988 | VLDB | 0.00020359891 |