DBScholar

Back to papers

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)

Paper ID
7353
Venue
SIGMOD
Year
2025
Pagerank
5.3251649e-05
Overall Rank
9,052 | 37.90%
DOI
10.1145/3725403

Incoming Non-self Citations Over Time

Authors

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)

Showing 1 of 1 citing papers.

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
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 12 of 12 cited papers.

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

Previous Page 1 / 1 Next

Semantically Similar Papers