Learned Cardinalities: Estimating Correlated Joins with Deep Learning
Summary: Proposes MSCN, a multi-set convolutional network that encodes relational query plans with set semantics to learn cardinalities and capture join-crossing correlations. Combines deep learning with sampling to handle zero-sample cases, yielding much better estimates on real-world data. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Andreas Kipf (Technical University of Munich)
- 2. Thomas Kipf (University of Amsterdam)
- 3. Bernhard Radke (Technical University of Munich)
- 4. Viktor Leis (Technical University of Munich)
- 5. Peter Boncz (Centrum Wiskunde & Informatica)
- 6. Alfons Kemper (Technical University of Munich)
BibTeX Citation
@inproceedings{kipf_cidr19,
address = {Amsterdam, Netherlands},
series = {{CIDR} '19},
title = {{Learned Cardinalities: Estimating Correlated Joins with Deep Learning}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Kipf, Andreas and Kipf, Thomas and Radke, Bernhard and Leis, Viktor and Boncz, Peter and Kemper, Alfons},
year = {2019}
}Incoming Citations (Sorted by Pagerank)
Showing 35 of 185 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 14 of 14 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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