Query Optimization for Dynamic Imputation
Summary: ImputeDB fuses imputation with a cost-based optimizer to do on-the-fly cleaning per query. Choosing where to apply imputations yields 10–140x speedups over pre-imputation, with 0–8% result change and 0–21% data loss versus dropping missing values. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. José Cambronero
- 2. John K. Feser
- 3. Micah J. Smith
- 4. Samuel Madden
Incoming Citations (Sorted by Pagerank)
Showing 18 of 18 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,659 | Multi-Objective Parametric Query Optimization | 2015 | VLDB | 8.3604734e-05 |
| 3,445 | Processing Forecasting Queries | 2007 | VLDB | 7.08644e-05 |
| 5,549 | Query Processing over Incomplete Autonomous Databases | 2007 | VLDB | 5.4428494e-05 |
| 5,779 | Lenses: An On-Demand Approach to ETL | 2015 | VLDB | 5.3307398e-05 |
| 6,487 | Inter-Operator Feedback in Data Stream Management Systems via Punctuation | 2009 | CIDR | 5.0435729e-05 |
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