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Exact Processing of Uncertain Top-k Queries in Multi-criteria Settings
Summary: Proposes uncertain top-k queries (UTK) for multi-criteria ranking under bounded weight uncertainty, reporting options that may belong to the top-k. Extends to exact top-k per weight setting with a scalable processing framework and benchmarks on standard datasets.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 11790
- Venue
- VLDB
- Year
- 2018
- Pagerank
- 5.0851965e-05
- Overall Rank
- 6,387 | 55.57%
- DOI
-
10.14778/3204028.3204031
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 9 of 9 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 21 of 21 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 2 |
R-Trees: A Dynamic Index Structure For Spatial Searching |
1984 |
SIGMOD |
0.0032169493 |
| 430 |
The Onion Technique: Indexing for Linear Optimization Queries |
2000 |
SIGMOD |
0.00023463938 |
| 1,072 |
Regret-Minimizing Representative Databases |
2010 |
VLDB |
0.00014270817 |
| 1,275 |
Continuous Nearest Neighbor Search |
2002 |
VLDB |
0.00012883899 |
| 1,609 |
A Unified Approach to Ranking in Probabilistic Databases |
2009 |
VLDB |
0.00011150935 |
| 1,707 |
Ranking Queries on Uncertain Data: A Probabilistic Threshold Approach |
2008 |
SIGMOD |
0.00010816111 |
| 1,998 |
Discovering Relative Importance of Skyline Attributes |
2009 |
VLDB |
9.824482e-05 |
| 2,478 |
Computing k-Regret Minimizing Sets |
2014 |
VLDB |
8.6927744e-05 |
| 2,976 |
Processing a Large Number of Continuous Preference Top-k Queries |
2012 |
SIGMOD |
7.789303e-05 |
| 3,014 |
Ranking with Uncertain Scoring Functions: Semantics and Sensitivity Measures |
2011 |
SIGMOD |
7.70946e-05 |
| 3,185 |
Top-k Queries on Uncertain Data: On Score Distribution and Typical Answers |
2009 |
SIGMOD |
7.4192604e-05 |
| 3,691 |
Kernel-Based Skyline Cardinality Estimation |
2009 |
SIGMOD |
6.8383587e-05 |
| 4,095 |
Ranking Continuous Probabilistic Datasets |
2010 |
VLDB |
6.4556768e-05 |
| 4,564 |
Learning User Preferences By Adaptive Pairwise Comparison |
2015 |
VLDB |
6.0819005e-05 |
| 6,091 |
Reconciling Skyline and Ranking Queries |
2017 |
VLDB |
5.214376e-05 |
| 6,203 |
Maximum Rank Query |
2015 |
VLDB |
5.1590738e-05 |
| 6,391 |
k-Hit Query: Top-k Query with Probabilistic Utility Function |
2015 |
SIGMOD |
5.0842079e-05 |
| 6,632 |
Global Immutable Region Computation |
2014 |
SIGMOD |
4.984576e-05 |
| 7,540 |
Boosting Spatial Pruning: On Optimal Pruning of MBRs |
2010 |
SIGMOD |
4.7159549e-05 |
| 8,654 |
Finding Pareto Optimal Groups: Group-based Skyline |
2015 |
VLDB |
4.4751356e-05 |
| 8,825 |
Determining the Impact Regions of Competing Options in Preference Space |
2017 |
SIGMOD |
4.4415078e-05 |
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| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 12,208 |
On Pruning for Top-K Ranking in Uncertain Databases |
2011 |
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4.1945683e-05 |
| 11,195 |
rkHit: Representative Query with Uncertain Preference |
2023 |
SIGMOD |
4.1945683e-05 |
| 1,707 |
Ranking Queries on Uncertain Data: A Probabilistic Threshold Approach |
2008 |
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| 6,391 |
k-Hit Query: Top-k Query with Probabilistic Utility Function |
2015 |
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| 7,963 |
Efficient Top-K Processing Over Query-Dependent Functions |
2008 |
VLDB |
4.613363e-05 |
| 2,976 |
Processing a Large Number of Continuous Preference Top-k Queries |
2012 |
SIGMOD |
7.789303e-05 |
| 3,185 |
Top-k Queries on Uncertain Data: On Score Distribution and Typical Answers |
2009 |
SIGMOD |
7.4192604e-05 |
| 8,372 |
URank: Formulation and Efficient Evaluation of Top-k Queries in Uncertain Databases |
2007 |
SIGMOD |
4.532996e-05 |
| 10,364 |
A Rank-Based Approach to Recommender System’s Top-K Queries with Uncertain Scores |
2025 |
SIGMOD |
4.1945683e-05 |
| 3,014 |
Ranking with Uncertain Scoring Functions: Semantics and Sensitivity Measures |
2011 |
SIGMOD |
7.70946e-05 |