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Epsilon Grid Order: An Algorithm for the Similarity Join on Massive High-Dimensional Data

Summary: Proposes Epsilon Grid Order, a scalable similarity-join for massive high-dimensional data using an equi-distant grid-order. Achieves memory efficiency with external sorting and a join-phase schedule, beating MSJ and epsilon-kdB-tree on large datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3346
Venue
SIGMOD
Year
2001
Pagerank
9.3421788e-05
Overall Rank
1,993 | 86.33%
DOI
10.1145/375663.375714

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{bohm_sigmod01,
        title = {{Epsilon Grid Order: An Algorithm for the Similarity Join on Massive High-Dimensional Data}},
        author = {Böhm, Christian and Braunmüller, Bernhard and Krebs, Florian and Kriegel, Hans-Peter},
        series = {{SIGMOD} '01},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/375663.375714},
        url = {https://dl.acm.org/doi/10.1145/375663.375714},
        year = {2001}
}

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