Verdict: A System for Stochastic Query Planning
Summary: Verdict: an approximate query-processing system that provides immediate, interactive answers using carefully chosen samples with statistical error bounds. Novelty: stochastic query planning that schedules execution over samples to guarantee interactivity at any scale, trading exactness for bounded error. (summarized by gpt-5-mini on Feb 09 2026)
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No non-self incoming citations found for this paper in this database.
Authors
- 1. Barzan Mozafari (University of Michigan)
BibTeX Citation
@inproceedings{mozafari_cidr15,
address = {Amsterdam, Netherlands},
series = {{CIDR} '15},
title = {{Verdict: A System for Stochastic Query Planning}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Mozafari, Barzan},
year = {2015}
}Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 784 | VerdictDB: Universalizing Approximate Query Processing | 2018 | SIGMOD | 0.00014012614 |
| 2,027 | Database Learning: Toward a Database that Becomes Smarter Every Time | 2017 | SIGMOD | 9.1618139e-05 |
| 6,004 | Approximate Query Engines: Commercial Challenges and Research Opportunities | 2017 | SIGMOD | 5.9166815e-05 |
| 12,215 | Demonstration of VerdictDB, the Platform-Independent AQP System | 2018 | SIGMOD | 4.9793485e-05 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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