Efficient Indexing Methods for Probabilistic Threshold Queries over Uncertain Data
Summary: Two index schemes for probabilistic threshold queries over uncertain data. The first augments uncertainty into an R-tree, illustrating the hardness of interval-with-probability indexing under 1D→2D mapping; the second uses variance-based clustering to group similar uncertainty, enabling near-optimal PTQ performance across diverse PDFs. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Reynold Cheng
- 2. Yuni Xia
- 3. Sunil Prabhakar
- 4. Rahul Shah
- 5. Jeffrey Scott Vitter
Incoming Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 467 | Evaluating Probabilistic Queries over Imprecise Data | 2003 | SIGMOD | 0.00022443768 |
| 631 | Indexing the Positions of Continuously Moving Objects | 2000 | SIGMOD | 0.00018935493 |
| 986 | Managing Intervals Efficiently in Object-Relational Databases | 2000 | VLDB | 0.00014838568 |
| 1,172 | Processing Queries By Linear Constraints | 1997 | PODS | 0.00013505441 |
| 1,182 | On Two-Dimensional Indexability and Optimal Range Search Indexing (Extended Abstract) | 1999 | PODS | 0.00013455963 |
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| 8,882 | Threshold Query Optimization for Uncertain Data | 2010 | SIGMOD | 4.4289641e-05 |
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