Sample-Efficient Cardinality Estimation Using Geometric Deep Learning
Summary: A sample-efficient learned cardinality estimator uses geometric deep learning over join graphs, predicate feature-selection encodings, and relational-algebra/three-valued-logic regularization without extra labels. It improves PostgreSQL end-to-end runtime under sparse training and workload shifts. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Silvan Reiner (University of Konstanz)
- 2. Michael Grossniklaus (University of Konstanz)
BibTeX Citation
@article{reiner_vldb24,
title = {{Sample-Efficient Cardinality Estimation Using Geometric Deep Learning}},
author = {Reiner, Silvan and Grossniklaus, Michael},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {4},
pages = {740--752},
doi = {10.14778/3636218.3636229},
url = {https://doi.org/10.14778/3636218.3636229},
year = {2024}
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