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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
5221
Venue
SIGMOD
Year
2016
Pagerank
4.909799e-05
Overall Rank
6,843 | 52.40%
DOI
10.1145/2882903.2914831

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Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
6,816 RRR: Rank-Regret Representative 2019 SIGMOD 4.9173197e-05
6,834 A Unified Optimization Algorithm For Solving "Regret-Minimizing Representative" Problems 2020 VLDB 4.9117328e-05
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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 0.00014270817
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