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
- 2. Dominik Olwig
- 3. Claudio Hartmann
- 4. Dirk Habich
- 5. Wolfgang Lehner
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,511 | Accurate Summary-based Cardinality Estimation Through the Lens of Cardinality Estimation Graphs | 2022 | VLDB | 7.0254052e-05 |
| 5,368 | Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing | 2022 | VLDB | 5.5457532e-05 |
| 8,617 | A Spark Optimizer for Adaptive, Fine-Grained Parameter Tuning | 2024 | VLDB | 4.4846425e-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 |
|---|---|---|---|---|
| 71 | How Good Are Query Optimizers, Really? | 2016 | VLDB | 0.00059038975 |
| 2,142 | Pessimistic Cardinality Estimation: Tighter Upper Bounds for Intermediate Join Cardinalities | 2019 | SIGMOD | 9.4507296e-05 |
| 3,725 | Estimating Cardinalities with Deep Sketches | 2019 | SIGMOD | 6.8170734e-05 |
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