Stochastic SketchRefine: Scaling In-Database Decision-Making under Uncertainty to Millions of Tuples
Summary: Risk-constraint linearization (RCL) compiles Stochastic Package Queries into ILPs whose size is independent of Monte Carlo scenario count, enabling feasible, near-optimal packages and richer SPaQL risk specs. Stochastic SketchRefine is a sketch-and-refine divide-and-conquer optimizer that partitions tuples to solve million-scale SPQs, producing high-quality packages with orders-of-magnitude runtime reductions over prior solvers. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Riddho R. Haque
- 2. Anh L. Mai
- 3. Matteo Brucato
- 4. Azza Abouzied
- 5. Peter J. Haas
- 6. Alexandra Meliou
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,664 | Efficient Answering of Historical What-if Queries | 2022 | SIGMOD | 6.0127053e-05 |
| 6,019 | Uncertain Centroid based Partitional Clustering of Uncertain Data | 2012 | VLDB | 5.2415551e-05 |
| 7,888 | Scalable Package Queries in Relational Database Systems | 2016 | VLDB | 4.6254226e-05 |
| 8,021 | Scaling Package Queries to a Billion Tuples via Hierarchical Partitioning and Customized Optimization | 2024 | VLDB | 4.6039546e-05 |
| 8,705 | Stochastic Package Queries in Probabilistic Databases | 2020 | SIGMOD | 4.4646588e-05 |
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