Representative Query Results by Voting

Summary: Introduces SimSTV to select a representative subset S of T that mirrors its value distribution and proportion, not diversity. Voting-theory-inspired, efficient algorithm with extensions (e.g., affirmative action); experiments validate effectiveness. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 6372
Venue: SIGMOD
Year: 2022
Pagerank: 5.3058708e-05
Overall Rank: 9,197 | 36.91%
DOI: 10.1145/3514221.3517858

Incoming Non-self Citations Over Time

Authors

1. Rachel Behar (Hebrew University)
2. Sara Cohen (Hebrew University)

BibTeX Citation

@inproceedings{behar_sigmod22,
        title = {{Representative Query Results by Voting}},
        author = {Behar, Rachel and Cohen, Sara},
        series = {{SIGMOD} '22},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3514221.3517858},
        url = {https://dl.acm.org/doi/10.1145/3514221.3517858},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,201	Balancing Global and Local: Representative Sampling for Large-Scale Vector Data	2026	SIGMOD	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
5	Optimal Aggregation Algorithms for Middleware [Extended Abstract]	2001	PODS	0.0010828372
1,597	Designing Fair Ranking Schemes	2019	SIGMOD	0.00010246472
3,596	On the Complexity of Query Result Diversification	2013	VLDB	7.2722683e-05

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