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)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Riddho R. Haque (University of Massachusetts Amherst)
- 2. Anh L. Mai (New York University)
- 3. Matteo Brucato (Microsoft)
- 4. Azza Abouzied (New York University)
- 5. Peter J. Haas (University of Massachusetts Amherst)
- 6. Alexandra Meliou (University of Massachusetts Amherst)
BibTeX Citation
@article{haque_vldb25,
title = {{Stochastic SketchRefine: Scaling In-Database Decision-Making under Uncertainty to Millions of Tuples}},
author = {Haque, Riddho R. and Mai, Anh L. and Brucato, Matteo and Abouzied, Azza and Haas, Peter J. and Meliou, Alexandra},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {9},
pages = {3106--3118},
doi = {10.14778/3746405.3746431},
url = {https://doi.org/10.14778/3746405.3746431},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,822 | Decisionhouse: Prescriptive Analytics in the Data Stack | 2026 | VLDB | 4.9793485e-05 |
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 |
|---|---|---|---|---|
| 4,919 | Efficient Answering of Historical What-if Queries | 2022 | SIGMOD | 6.3544814e-05 |
| 6,960 | Scalable Package Queries in Relational Database Systems | 2016 | VLDB | 5.6333868e-05 |
| 7,114 | Uncertain Centroid based Partitional Clustering of Uncertain Data | 2012 | VLDB | 5.601767e-05 |
| 8,128 | Scaling Package Queries to a Billion Tuples via Hierarchical Partitioning and Customized Optimization | 2024 | VLDB | 5.3939686e-05 |
| 9,050 | Stochastic Package Queries in Probabilistic Databases | 2020 | SIGMOD | 5.2305429e-05 |
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