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 |
|---|---|---|---|---|
| 463 | Evaluating Probabilistic Queries over Imprecise Data | 2003 | SIGMOD | 0.00022497894 |
| 628 | Indexing the Positions of Continuously Moving Objects | 2000 | SIGMOD | 0.00018957327 |
| 986 | Managing Intervals Efficiently in Object-Relational Databases | 2000 | VLDB | 0.00014825631 |
| 1,171 | Processing Queries By Linear Constraints | 1997 | PODS | 0.00013507216 |
| 1,183 | On Two-Dimensional Indexability and Optimal Range Search Indexing (Extended Abstract) | 1999 | PODS | 0.00013444058 |
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| 463 | Evaluating Probabilistic Queries over Imprecise Data | 2003 | SIGMOD | 0.00022497894 |
| 11,832 | Range-Max Queries on Uncertain Data | 2016 | PODS | 4.1905499e-05 |
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