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ATLAS: A Probabilistic Algorithm for High Dimensional Similarity Search

Summary: ATLAS: probabilistic high-dimensional similarity search for binary vectors. Employs truly random permutations to filter candidates and estimate similarity, achieving 97.5% recall and up to 100x speedups over exact/approx methods. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4511
Venue
SIGMOD
Year
2011
Pagerank
9.4560124e-05
Overall Rank
1,935 | 86.73%
DOI
10.1145/1989323.1989428

Incoming Non-self Citations Over Time

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BibTeX Citation

@inproceedings{zhai_sigmod11,
        title = {{ATLAS: A Probabilistic Algorithm for High Dimensional Similarity Search}},
        author = {Zhai, Jiaqi and Lou, Yin and Gehrke, Johannes},
        series = {{SIGMOD} '11},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1989323.1989428},
        url = {https://dl.acm.org/doi/10.1145/1989323.1989428},
        year = {2011}
}

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