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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
4,264 A Method for Optimizing Opaque Filter Queries 2020 SIGMOD 6.7937529e-05
4,349 ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads 2024 VLDB 6.7504619e-05
4,368 Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process 2022 SIGMOD 6.7393882e-05
4,468 One Model to Rule them All: Towards Zero-Shot Learning for Databases 2022 CIDR 6.6819041e-05
4,470 Kepler: Robust Learning for Faster Parametric Query Optimization 2023 SIGMOD 6.6817353e-05
4,603 The Art of Balance: A RateupDB Experience of Building a CPU/GPU Hybrid Database Product 2021 VLDB 6.6105578e-05
4,612 Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts 2022 SIGMOD 6.6072026e-05
4,617 Learned Cardinality Estimation for Similarity Queries 2021 SIGMOD 6.604437e-05
4,643 Learned Index Benefits: Machine Learning Based Index Performance Estimation 2022 VLDB 6.5907466e-05
4,671 PreQR: Pre-training Representation for SQL Understanding 2022 SIGMOD 6.5732787e-05
4,780 Can Learned Models Replace Hash Functions? 2023 VLDB 6.5118885e-05
4,789 Learned Approximate Query Processing: Make it Light, Accurate and Fast 2021 CIDR 6.5072039e-05
4,900 COMPASS: Online Sketch-based Query Optimization for In-Memory Databases 2021 SIGMOD 6.4534715e-05
4,929 AutoSteer: Learned Query Optimization for Any SQL Database 2023 VLDB 6.4423294e-05
5,010 Analyzing the Impact of Cardinality Estimation on Execution Plans in Microsoft SQL Server 2023 VLDB 6.4023732e-05
5,011 Monotonic Cardinality Estimation of Similarity Selection: A Deep Learning Approach 2020 SIGMOD 6.4020848e-05
5,059 Steering Query Optimizers: A Practical Take on Big Data Workloads 2021 SIGMOD 6.3807509e-05
5,105 SAM: Database Generation from Query Workloads with Supervised Autoregressive Models 2022 SIGMOD 6.3628539e-05
5,148 LSched: A Workload-Aware Learned Query Scheduler for Analytical Database Systems 2022 SIGMOD 6.3465986e-05
5,277 FASTgres: Making Learned Query Optimizer Hinting Effective 2023 VLDB 6.2859099e-05
5,340 Machine Learning for Databases 2021 VLDB 6.2603359e-05
5,529 Debunking the Myth of Join Ordering: Toward Robust SQL Analytics 2025 SIGMOD 6.18591e-05
5,558 HMAB: Self-Driving Hierarchy of Bandits for Integrated Physical Database Design Tuning 2023 VLDB 6.1749098e-05
5,573 Eraser: Eliminating Performance Regression on Learned Query Optimizer 2024 VLDB 6.1682747e-05
5,576 SafeBound: A Practical System for Generating Cardinality Bounds 2023 SIGMOD 6.1663946e-05
5,639 LpBound: Pessimistic Cardinality Estimation using ℓp-Norms of Degree Sequences 2025 SIGMOD 6.1385102e-05
5,712 Sample-Efficient Cardinality Estimation Using Geometric Deep Learning 2024 VLDB 6.1123894e-05
5,743 Joins on Samples: A Theoretical Guide for Practitioners 2020 VLDB 6.1025457e-05
5,744 SQLStorm: Taking Database Benchmarking into the LLM Era 2025 VLDB 6.1019672e-05
5,767 A Comparative Study and Component Analysis of Query Plan Representation Techniques in ML4DB Studies 2024 VLDB 6.0945741e-05
5,792 Pre-training Summarization Models of Structured Datasets for Cardinality Estimation 2022 VLDB 6.0871213e-05
5,974 Towards instance-optimized data systems 2021 VLDB 6.0230488e-05
5,978 From Auto-tuning One Size Fits All to Self-designed and Learned Data-intensive Systems 2019 SIGMOD 6.0212877e-05
6,024 Expand your Training Limits! Generating Training Data for ML-based Data Management 2021 SIGMOD 6.0031118e-05
6,088 How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks 2025 SIGMOD 5.9813965e-05
6,132 Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data 2023 SIGMOD 5.9660278e-05
6,323 Modeling Shifting Workloads for Learned Database Systems 2024 SIGMOD 5.9141228e-05
6,327 Breaking It Down: An In-depth Study of Index Advisors 2024 VLDB 5.9124005e-05
6,341 Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks 2024 SIGMOD 5.9068986e-05
6,357 A Unified Transferable Model for ML-Enhanced DBMS 2022 CIDR 5.9020843e-05
6,462 PilotScope: Steering Databases with Machine Learning Drivers 2024 VLDB 5.8717744e-05
6,543 Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation 2023 SIGMOD 5.8461929e-05
6,593 Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis 2023 VLDB 5.8297039e-05
6,704 ASM: Harmonizing Autoregressive Model, Sampling, and Multi-dimensional Statistics Merging for Cardinality Estimation 2024 SIGMOD 5.797374e-05
6,760 LPLM: A Neural Language Model for Cardinality Estimation of LIKE-Queries 2024 SIGMOD 5.7826781e-05
6,921 Rethinking Learned Cost Models: Why Start from Scratch? 2023 SIGMOD 5.7388557e-05
6,939 Multi-Tenant Cloud Data Services: State-of-the-Art, Challenges and Opportunities 2022 SIGMOD 5.7338637e-05
7,048 Learning to Sample: Counting with Complex Queries 2020 VLDB 5.7178054e-05
7,193 dbET: Execution Time Distribution-based Plan Selection 2023 SIGMOD 5.6770249e-05
7,206 Selectivity Functions of Range Queries are Learnable* 2022 SIGMOD 5.6731116e-05
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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.

Rank Cited Paper Year Venue Pagerank
18 How Good Are Query Optimizers, Really? 2016 VLDB 0.00059284255
43 The Case for Learned Index Structures 2018 SIGMOD 0.00046060254
86 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00035316107
101 Selectivity Estimation Without the Attribute Value Independence Assumption 1997 VLDB 0.00034376651
388 Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors 2009 VLDB 0.00019410042
682 Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques 2012 VLDB 0.00015014887
694 Cardinality Estimation Done Right: Index-Based Join Sampling 2017 CIDR 0.00014911698
737 Join Size Estimation Subject to Filter Conditions 2015 VLDB 0.00014490983
1,256 Sampling-Based Query Re-Optimization 2016 SIGMOD 0.00011457194
1,286 Adaptive Optimization of Very Large Join Queries 2018 SIGMOD 0.00011320736
1,536 Improved Selectivity Estimation by Combining Knowledge from Sampling and Synopses 2018 VLDB 0.00010460864
1,664 Two-Level Sampling for Join Size Estimation 2017 SIGMOD 0.00010070362
2,157 A Black-Box Approach to Query Cardinality Estimation 2007 CIDR 9.0625592e-05
2,281 Selectivity Estimation in Extensible Databases - A Neural Network Approach 1998 VLDB 8.8129279e-05
Previous Page 1 / 1 Next

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