Optimizing Probabilistic Query Processing on Continuous Uncertain Data
Summary: Optimizes probabilistic threshold queries on continuous uncertain data with (i) new indexes to speed joins, (ii) dimensionality reduction and fast filters for selections, and (iii) a dynamic per-tuple query plan. Empirical results on real data and benchmarks show accuracy and substantial performance gains over state-of-the-art threshold optimizers. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Liping Peng
- 2. Yanlei Diao
- 3. Anna Liu
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,077 | Towards High-Throughput Gibbs Sampling at Scale: A Study across Storage Managers | 2013 | SIGMOD | 6.4678697e-05 |
| 8,433 | Supporting User-Defined Functions on Uncertain Data | 2013 | VLDB | 4.514792e-05 |
| 11,905 | Supporting Data Uncertainty in Array Databases | 2015 | SIGMOD | 4.1945683e-05 |
| 12,129 | Answering Queries using Views over Probabilistic XML: Complexity and Tractability | 2012 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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