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)
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Authors
- 1. Christian Böhm (University of Munich)
- 2. Bernhard Braunmüller (University of Munich)
- 3. Florian Krebs (University of Munich)
- 4. Hans-Peter Kriegel (University of Munich)
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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