PostCENN: PostgreSQL with Machine Learning Models for Cardinality Estimation
Summary: PostCENN inserts ML models as first-class PostgreSQL citizens to enhance cardinality estimation. An end-to-end lifecycle trains, deploys in the optimizer, and deletes models, blending ML with histograms for targeted schema portions. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Lucas Woltmann (Technical University of Dresden)
- 2. Dominik Olwig (Technical University of Dresden)
- 3. Claudio Hartmann (Technical University of Dresden)
- 4. Dirk Habich (Technical University of Dresden)
- 5. Wolfgang Lehner (Technical University of Dresden)
BibTeX Citation
@article{woltmann_vldb21,
title = {{PostCENN: PostgreSQL with Machine Learning Models for Cardinality Estimation}},
author = {Woltmann, Lucas and Olwig, Dominik and Hartmann, Claudio and Habich, Dirk and Lehner, Wolfgang},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {12},
pages = {2715--2718},
doi = {10.14778/3476311.3476327},
url = {https://doi.org/10.14778/3476311.3476327},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,070 | Accurate Summary-based Cardinality Estimation Through the Lens of Cardinality Estimation Graphs | 2022 | VLDB | 7.7900444e-05 |
| 5,388 | Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing | 2022 | VLDB | 6.2362811e-05 |
| 8,615 | A Spark Optimizer for Adaptive, Fine-Grained Parameter Tuning | 2024 | VLDB | 5.4005602e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 18 | How Good Are Query Optimizers, Really? | 2016 | VLDB | 0.00059284255 |
| 1,499 | Pessimistic Cardinality Estimation: Tighter Upper Bounds for Intermediate Join Cardinalities | 2019 | SIGMOD | 0.00010564536 |
| 3,213 | Estimating Cardinalities with Deep Sketches | 2019 | SIGMOD | 7.6328677e-05 |
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