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Efficient and Tunable Similar Set Retrieval

Summary: Formalizes similarity-based indexing for set-valued attributes, reduced to similarity-preserving binary vectors in Hamming space. Proposes two data-structure primitives and a tunable, constraint-driven index; prototype experiments on real datasets show accuracy–efficiency tradeoffs. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3335
Venue
SIGMOD
Year
2001
Pagerank
5.5181056e-05
Overall Rank
7,942 | 45.52%
DOI
10.1145/375663.375689

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{gionis_sigmod01,
        title = {{Efficient and Tunable Similar Set Retrieval}},
        author = {Gionis, Aristides and Gunopulos, Dimitrios and Koudas, Nick},
        series = {{SIGMOD} '01},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/375663.375689},
        url = {https://dl.acm.org/doi/10.1145/375663.375689},
        year = {2001}
}

Incoming Citations (Sorted by Pagerank)

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Rank Citing Paper Year Venue Pagerank
12,416 TACO: Tunable Approximate Computation of Outliers in Wireless Sensor Networks 2010 SIGMOD 5.093636e-05
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