Exploiting Correlations for Expensive Predicate Evaluation
Summary: Proposes cost-aware techniques for evaluating selection queries with UDF predicates by exploiting correlations and prior probabilities to meet user-specified precision/recall constraints. The methods handle known, noisy, or unknown probabilities, generalize to complex queries, and deliver up to 80% UDF savings with modest accuracy loss on real data. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 329 | Accelerating Machine Learning Inference with Probabilistic Predicates | 2018 | SIGMOD | 0.00027249545 |
| 6,590 | Interactive Demonstration of Probabilistic Predicates | 2018 | SIGMOD | 5.0010949e-05 |
| 7,251 | Learning to Sample: Counting with Complex Queries | 2020 | VLDB | 4.7890519e-05 |
| 7,283 | Sia: Optimizing Queries using Learned Predicates | 2021 | SIGMOD | 4.7764688e-05 |
| 9,049 | JENNER: Just-in-time Enrichment in Query Processing | 2022 | VLDB | 4.4039656e-05 |
| 9,435 | AMNES: Accelerating the computation of data correlation using FPGAs | 2023 | VLDB | 4.3430376e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 22 of 22 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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