Fast Approximate Similarity Join in Vector Databases
Summary: SimJoin exploits join-window reuse to speed up approximate similarity joins in vector databases, beating per-point range queries. Join-window order optimization, k-similarity support, and a proximity-graph index; experiments show large speedups. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jiadong Xie (Chinese University of Hong Kong)
- 2. Jeffrey Xu Yu (Chinese University of Hong Kong)
- 3. Yingfan Liu (Xidian University)
BibTeX Citation
@inproceedings{xie_sigmod25,
title = {{Fast Approximate Similarity Join in Vector Databases}},
author = {Xie, Jiadong and Yu, Jeffrey Xu and Liu, Yingfan},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3725403},
url = {https://dl.acm.org/doi/10.1145/3725403},
year = {2025}
}
Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,418 | MorphingDB: A Task-Centric AI-Native DBMS for Model Management and Inference | 2026 | SIGMOD | 5.093636e-05 |
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Showing 12 of 12 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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