Minimizing Average Regret Ratio in Database

Summary: Proposes ARR to select k representative points, basing satisfaction on utility distribution rather than max regret. Proves ARR is supermodular and enables approximation, yielding fixed-size, distribution-aware samples without user input, unlike k-regret. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 5283
Venue: SIGMOD
Year: 2016
Pagerank: 5.7460174e-05
Overall Rank: 6,886 | 52.76%
DOI: 10.1145/2882903.2914831

Incoming Non-self Citations Over Time

Authors

1. Sepanta Zeighami (Hong Kong University of Science and Technology)
2. Raymond Chi-Wing Wong (Hong Kong University of Science and Technology)

BibTeX Citation

@inproceedings{zeighami_sigmod16,
        title = {{Minimizing Average Regret Ratio in Database}},
        author = {Zeighami, Sepanta and Wong, Raymond Chi-Wing},
        series = {{SIGMOD} '16},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2882903.2914831},
        url = {https://dl.acm.org/doi/10.1145/2882903.2914831},
        year = {2016}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
6,592	RRR: Rank-Regret Representative	2019	SIGMOD	5.830041e-05
6,786	A Unified Optimization Algorithm For Solving “Regret-Minimizing Representative” Problems	2020	VLDB	5.7740702e-05

Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
1,072	Regret-Minimizing Representative Databases	2010	VLDB	0.0001230281

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