Cleaning Uncertain Data with Quality Guarantees
Summary: Proposes PWS-quality, an ambiguity metric for probabilistic databases under possible-world semantics. Develops polynomial-time optimal cleaning to maximize PWS-quality by probing uncertain objects; efficient evaluation for independent and ranking queries, with practical heuristics. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Reynold Cheng
- 2. Jinchuan Chen
- 3. Xike Xie
Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,699 | Sensitivity Analysis and Explanations for Robust Query Evaluation in Probabilistic Databases | 2011 | SIGMOD | 0.00010858983 |
| 3,051 | Partial Results in Database Systems | 2014 | SIGMOD | 7.6512591e-05 |
| 6,182 | Top-K Deep Video Analytics: A Probabilistic Approach | 2021 | SIGMOD | 5.1682689e-05 |
| 6,670 | Explore or Exploit? Effective Strategies for Disambiguating Large Databases | 2010 | VLDB | 4.9672601e-05 |
| 9,043 | Query-Guided Resolution in Uncertain Databases | 2023 | SIGMOD | 4.4039656e-05 |
| 9,054 | Selecting Data to Clean for Fact Checking: Minimizing Uncertainty vs. Maximizing Surprise | 2019 | VLDB | 4.4039656e-05 |
| 11,178 | LinCQA: Faster Consistent Query Answering with Linear Time Guarantees | 2023 | SIGMOD | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 12 of 12 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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