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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)

Paper ID
314
Venue
CIDR
Year
2019
Pagerank
0.00035838391
Overall Rank
84 | 99.43%
DOI
-

Incoming Non-self Citations Over Time

Authors

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 50 of 180 citing papers.

Rank Citing Paper Year Venue Pagerank
154 Neo: A Learned Query Optimizer 2019 VLDB 0.00028726181
323 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021264788
378 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00019638121
401 Deep Unsupervised Cardinality Estimation 2020 VLDB 0.00019092557
447 ALEX: An Updatable Adaptive Learned Index 2020 SIGMOD 0.00018322593
465 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.0001803934
513 NeuroCard: One Cardinality Estimator for All Tables 2021 VLDB 0.00017190574
563 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.0001650812
568 SageDB: A Learned Database System 2019 CIDR 0.0001641553
697 Selectivity Estimation for Range Predicates using Lightweight Models 2019 VLDB 0.00014888851
1,061 Are We Ready For Learned Cardinality Estimation? 2021 VLDB 0.00012369764
1,122 Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation 2022 VLDB 0.0001209124
1,170 QuickSel: Quick Selectivity Learning with Mixture Models 2020 SIGMOD 0.00011827259
1,241 Balsa: Learning a Query Optimizer Without Expert Demonstrations 2022 SIGMOD 0.00011521639
1,573 Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries 2020 SIGMOD 0.00010328171
1,832 DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems 2021 VLDB 9.6607418e-05
1,876 Flow-Loss: Learning Cardinality Estimates That Matter 2021 VLDB 9.5717543e-05
1,982 WeTune: Automatic Discovery and Verification of Query Rewrite Rules 2022 SIGMOD 9.3573897e-05
1,988 FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation 2021 VLDB 9.3501502e-05
2,213 Designing Succinct Secondary Indexing Mechanism by Exploiting Column Correlations 2019 SIGMOD 8.9410226e-05
2,355 QueryFormer: A Tree Transformer Model for Query Plan Representation 2022 VLDB 8.7022189e-05
2,420 Lero: A Learning-to-Rank Query Optimizer 2023 VLDB 8.605257e-05
2,452 A Learned Query Rewrite System using Monte Carlo Tree Search 2022 VLDB 8.5584e-05
2,499 Learning a Partitioning Advisor for Cloud Databases 2020 SIGMOD 8.4993549e-05
2,521 CodexDB: Synthesizing Code for Query Processing from Natural Language Instructions using GPT-3 Codex 2022 VLDB 8.4729505e-05
2,543 Learned Cardinality Estimation: An In-depth Study 2022 SIGMOD 8.4445934e-05
2,620 Fauce: Fast and Accurate Deep Ensembles with Uncertainty for Cardinality Estimation 2021 VLDB 8.3363963e-05
2,723 Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation 2022 VLDB 8.2049453e-05
2,762 Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection 2022 VLDB 8.1539867e-05
2,822 Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings 2020 SIGMOD 8.0898536e-05
2,844 Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction 2022 VLDB 8.0608767e-05
2,991 FactorJoin: A New Cardinality Estimation Framework for Join Queries 2023 SIGMOD 7.8880723e-05
3,035 Instance-Optimized Data Layouts for Cloud Analytics Workloads 2021 SIGMOD 7.8297746e-05
3,086 A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation 2021 SIGMOD 7.7708642e-05
3,162 Efficiently Approximating Selectivity Functions using Low Overhead Regression Models 2020 VLDB 7.6785856e-05
3,213 Estimating Cardinalities with Deep Sketches 2019 SIGMOD 7.6328677e-05
3,228 Correlation Sketches for Approximate Join-Correlation Queries 2021 SIGMOD 7.6215176e-05
3,283 A Learned Sketch for Subgraph Counting 2021 SIGMOD 7.56675e-05
3,545 Astrid: Accurate Selectivity Estimation for String Predicates using Deep Learning 2021 VLDB 7.3249967e-05
3,662 openGauss: An Autonomous Database System 2021 VLDB 7.2166682e-05
3,688 FACE: A Normalizing Flow based Cardinality Estimator 2022 VLDB 7.201795e-05
3,729 CARMI: A Cache-Aware Learned Index with a Cost-based Construction Algorithm 2022 VLDB 7.1683974e-05
3,769 Proving Query Equivalence Using Linear Integer Arithmetic 2023 SIGMOD 7.1403543e-05
3,809 Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift 2023 SIGMOD 7.1074195e-05
3,865 The Case for a Learned Sorting Algorithm 2020 SIGMOD 7.0621718e-05
3,926 UDO: Universal Database Optimization using Reinforcement Learning 2021 VLDB 7.0128068e-05
3,953 Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload 2021 SIGMOD 6.996368e-05
3,959 Simplicity Done Right for Join Ordering 2021 CIDR 6.9879431e-05
3,961 MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems 2021 SIGMOD 6.987575e-05
4,011 Towards Dynamic and Safe Configuration Tuning for Cloud Databases 2022 SIGMOD 6.959982e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 14 of 14 cited papers.

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