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Efficient Similarity Join and Search on Multi-Attribute Data

Summary: Introduces a prefix tree index enabling holistic pruning across multiple attributes for similarity join and search. With a cost model, greedy and budget-based multi-tree strategies plus a hybrid verifier yield strong empirical gains. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5015
Venue
SIGMOD
Year
2015
Pagerank
5.7418509e-05
Overall Rank
6,906 | 52.62%
DOI
10.1145/2723372.2723733

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{li_sigmod15,
        title = {{Efficient Similarity Join and Search on Multi-Attribute Data}},
        author = {Li, Guoliang and He, Jian and Deng, Dong and Li, Jian},
        series = {{SIGMOD} '15},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2723372.2723733},
        url = {https://dl.acm.org/doi/10.1145/2723372.2723733},
        year = {2015}
}

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