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RRR: Rank-Regret Representative

Summary: Rank-Regret Representative (RRR): minimal subset ensuring at least one of the top-k items for every possible ranking function (rank-based regret). NP-hard; uses geometric bounds and combinatorial approximations to compute compact, scalable representatives, validated on real datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5611
Venue
SIGMOD
Year
2019
Pagerank
4.9173197e-05
Overall Rank
6,816 | 52.59%
DOI
10.1145/3299869.3300080

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Showing 9 of 9 citing papers.

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Showing 14 of 14 cited papers.

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

Rank Cited Paper Year Venue Pagerank
7 Optimal Aggregation Algorithms for Middleware [Extended Abstract] 2001 PODS 0.0015496097
430 The Onion Technique: Indexing for Linear Optimization Queries 2000 SIGMOD 0.00023463938
914 Finding k-Dominant Skylines in High Dimensional Space 2006 SIGMOD 0.00015387584
1,072 Regret-Minimizing Representative Databases 2010 VLDB 0.00014270817
1,597 Designing Fair Ranking Schemes 2019 SIGMOD 0.00011209846
2,478 Computing k-Regret Minimizing Sets 2014 VLDB 8.6927744e-05
2,615 Interactive Regret Minimization 2012 SIGMOD 8.4473503e-05
2,933 Answering Top-k Queries Using Views 2006 VLDB 7.8679669e-05
3,463 Towards Robust Indexing for Ranked Queries 2006 VLDB 7.069675e-05
5,116 Efficient Computation of Regret-ratio Minimizing Set: A Compact Maxima Representative 2017 SIGMOD 5.6830089e-05
5,904 k-Regret Queries with Nonlinear Utilities 2015 VLDB 5.2790141e-05
6,843 Minimizing Average Regret Ratio in Database 2016 SIGMOD 4.909799e-05
8,129 Discovering the Skyline of Web Databases 2016 VLDB 4.5784968e-05
11,883 Query Reranking As A Service 2016 VLDB 4.1945683e-05
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