Fast and Reliable Missing Data Contingency Analysis with Predicate-Constraints
Summary: Contingency analysis under predicate-constraints bounds aggregates (SUM, COUNT, AVG, MIN, MAX) with missing data. An integer-program optimizer reconciles overlapping constraints to yield guaranteed hard bounds; experiments show competitive accuracy vs baselines. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xi Liang
- 2. Zechao Shang
- 3. Sanjay Krishnan
- 4. Aaron J. Elmore
- 5. Michael J. Franklin
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,724 | Combining Aggregation and Sampling (Nearly) Optimally for Approximate Query Processing | 2021 | SIGMOD | 4.9449472e-05 |
| 7,944 | Efficient Uncertainty Tracking for Complex Queries with Attribute-level Bounds | 2021 | SIGMOD | 4.6089395e-05 |
| 9,044 | Efficient Approximation of Certain and Possible Answers for Ranking and Window Queries over Uncertain Data | 2023 | VLDB | 4.3997447e-05 |
| 9,855 | In-Database Data Imputation | 2024 | SIGMOD | 4.2652623e-05 |
| 11,225 | Splitting Tuples of Mismatched Entities | 2023 | SIGMOD | 4.1905499e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 14 of 14 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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