Ed-Join: An Efficient Algorithm for Similarity Joins With Edit Distance Constraints
Summary: Ed-Join reframes edit-distance similarity joins around mismatching, rather than matching, q-grams. It derives location- and content-aware lower bounds and uses them for stronger candidate filtering, substantially outperforming prior methods on large real datasets. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Chuan Xiao (University of New South Wales)
- 2. Wei Wang (University of New South Wales)
- 3. Xuemin Lin (University of New South Wales)
BibTeX Citation
@article{xiao_vldb08,
title = {{Ed-Join: An Efficient Algorithm for Similarity Joins With Edit Distance Constraints}},
author = {Xiao, Chuan and Wang, Wei and Lin, Xuemin},
journal = {PVLDB},
series = {{VLDB} '08},
volume = {1},
number = {1},
pages = {933--944},
doi = {10.14778/1454159.1454260},
url = {https://doi.org/10.14778/1454159.1454260},
year = {2008}
}
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