Ranking Distributed Probabilistic Data
Summary: Top-k ranking for distributed probabilistic data, using the expected rank across possible worlds as the criterion. Designs both communication- and computation-efficient algorithms to obtain the global top-k with minimum data transfer. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Feifei Li
- 2. Ke Yi
- 3. Jeffrey Jestes
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,882 | Threshold Query Optimization for Uncertain Data | 2010 | SIGMOD | 4.4289641e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 17 of 17 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 745 | Distributed Top-K Monitoring | 2003 | SIGMOD | 0.00017330487 |
| 9,044 | Efficient Approximation of Certain and Possible Answers for Ranking and Window Queries over Uncertain Data | 2023 | VLDB | 4.4039656e-05 |
| 7,136 | Distributed Top-N Query Processing with Possibly Uncooperative Local Systems | 2003 | VLDB | 4.8220711e-05 |
| 3,185 | Top-k Queries on Uncertain Data: On Score Distribution and Typical Answers | 2009 | SIGMOD | 7.4192604e-05 |
| 10,364 | A Rank-Based Approach to Recommender System’s Top-K Queries with Uncertain Scores | 2025 | SIGMOD | 4.1945683e-05 |
| 12,208 | On Pruning for Top-K Ranking in Uncertain Databases | 2011 | VLDB | 4.1945683e-05 |
| 1,707 | Ranking Queries on Uncertain Data: A Probabilistic Threshold Approach | 2008 | SIGMOD | 0.00010816111 |
| 1,992 | Probabilistic Ranking of Database Query Results | 2004 | VLDB | 9.8462684e-05 |
| 4,095 | Ranking Continuous Probabilistic Datasets | 2010 | VLDB | 6.4556768e-05 |
| 1,609 | A Unified Approach to Ranking in Probabilistic Databases | 2009 | VLDB | 0.00011150935 |