THERMAL-JOIN: A Scalable Spatial Join for Dynamic Workloads
Summary: THERMAL-JOIN is a scalable spatial self-join for dynamic, memory-resident scientific simulations. A nested spatial grid identifies hot spots of nearby objects, yielding 8–12x speedups over state-of-the-art and scalable with higher precision. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Farhan Tauheed (Oracle)
- 2. Thomas Heinis (Imperial College London)
- 3. Anastasia Ailamaki (EPFL)
BibTeX Citation
@inproceedings{tauheed_sigmod15,
title = {{THERMAL-JOIN: A Scalable Spatial Join for Dynamic Workloads}},
author = {Tauheed, Farhan and Heinis, Thomas and Ailamaki, Anastasia},
series = {{SIGMOD} '15},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2723372.2749434},
url = {https://dl.acm.org/doi/10.1145/2723372.2749434},
year = {2015}
}
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,157 | Similarity Join over Array Data | 2016 | SIGMOD | 5.9554363e-05 |
| 9,871 | Finding Logic Bugs in Spatial Database Engines via Affine Equivalent Inputs | 2024 | SIGMOD | 5.2043672e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 17 of 17 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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