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A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation

Summary: Unified deep autoregressive UAE combines data and query signals to learn distributions for cardinality estimation. Progressive sampling via Gumbel-Softmax enables query learning; UAE yields tail error in single digits and higher accuracy with efficiency. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6172
Venue
SIGMOD
Year
2021
Pagerank
7.7708642e-05
Overall Rank
3,086 | 78.83%
DOI
10.1145/3448016.3452830

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{wu_sigmod21,
        title = {{A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation}},
        author = {Wu, Peizhi and Cong, Gao},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3452830},
        url = {https://dl.acm.org/doi/10.1145/3448016.3452830},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 34 of 34 citing papers.

Rank Citing Paper Year Venue Pagerank
1,122 Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation 2022 VLDB 0.0001209124
2,543 Learned Cardinality Estimation: An In-depth Study 2022 SIGMOD 8.4445934e-05
2,731 Neural Subgraph Counting with Wasserstein Estimator 2022 SIGMOD 8.1959181e-05
2,991 FactorJoin: A New Cardinality Estimation Framework for Join Queries 2023 SIGMOD 7.8880723e-05
3,338 Robust Query Driven Cardinality Estimation under Changing Workloads 2023 VLDB 7.5068221e-05
4,349 ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads 2024 VLDB 6.7504619e-05
5,105 SAM: Database Generation from Query Workloads with Supervised Autoregressive Models 2022 SIGMOD 6.3628539e-05
5,340 Machine Learning for Databases 2021 VLDB 6.2603359e-05
5,388 Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing 2022 VLDB 6.2362811e-05
5,529 Debunking the Myth of Join Ordering: Toward Robust SQL Analytics 2025 SIGMOD 6.18591e-05
5,576 SafeBound: A Practical System for Generating Cardinality Bounds 2023 SIGMOD 6.1663946e-05
6,323 Modeling Shifting Workloads for Learned Database Systems 2024 SIGMOD 5.9141228e-05
6,543 Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation 2023 SIGMOD 5.8461929e-05
6,704 ASM: Harmonizing Autoregressive Model, Sampling, and Multi-dimensional Statistics Merging for Cardinality Estimation 2024 SIGMOD 5.797374e-05
7,193 dbET: Execution Time Distribution-based Plan Selection 2023 SIGMOD 5.6770249e-05
8,040 PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost! 2021 VLDB 5.5018396e-05
8,492 ShadowAQP: Efficient Approximate Group-by and Join Query via Attribute-oriented Sample Size Allocation and Data Generation 2023 VLDB 5.4145838e-05
8,615 A Spark Optimizer for Adaptive, Fine-Grained Parameter Tuning 2024 VLDB 5.4005602e-05
9,101 NeuroSketch: Fast and Approximate Evaluation of Range Aggregate Queries with Neural Networks 2023 SIGMOD 5.324758e-05
9,192 Efficient and Effective Cardinality Estimation for Skyline Family 2023 SIGMOD 5.3058708e-05
9,428 LIMAO: A Framework for Lifelong Modular Learned Query Optimization 2025 VLDB 5.2709145e-05
9,465 Are Joins over LSM-trees Ready? Take RocksDB as an Example 2025 VLDB 5.2634238e-05
9,626 Spatial Query Optimization With Learning 2024 VLDB 5.2434488e-05
9,793 Selectivity Estimation for Queries Containing Predicates over Set-Valued Attributes 2023 SIGMOD 5.2193018e-05
9,920 Still Asking: How Good Are Query Optimizers, Really? 2025 VLDB 5.1955087e-05
9,958 A Practical Theory of Generalization in Selectivity Learning 2025 VLDB 5.1879626e-05
9,971 Athena: An Effective Learning-based Framework for Query Optimizer Performance Improvement 2025 SIGMOD 5.1845938e-05
10,028 PRICE: A Pretrained Model for Cross-Database Cardinality Estimation 2025 VLDB 5.1745962e-05
10,108 An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL 2025 SIGMOD 5.1347137e-05
10,486 Qualitative Join Discovery in Data Lakes using Examples 2026 SIGMOD 5.093636e-05
10,508 Practical Parameterized Query Optimization via Efficient Plan Reuse and List-wise Ranking 2026 SIGMOD 5.093636e-05
10,851 ACE: A Cardinality Estimator for Set-Valued Queries 2025 VLDB 5.093636e-05
10,875 Data-Agnostic Cardinality Learning from Imperfect Workloads 2025 VLDB 5.093636e-05
11,083 Graph Transformers for Query Plan Representation: Potentials and Challenges 2025 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 36 of 36 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
35 Improved Histograms for Selectivity Estimation of Range Predicates 1996 SIGMOD 0.00048481081
76 Practical Selectivity Estimation through Adaptive Sampling 1990 SIGMOD 0.00037054261
84 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035838391
100 LEO - DB2's LEarning Optimizer 2001 VLDB 0.00034385207
159 CORDS: Automatic Discovery of Correlations and Soft Functional Dependencies 2004 SIGMOD 0.00028129426
280 Selectivity Estimation using Probabilistic Models 2001 SIGMOD 0.00022454217
323 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021264788
365 STHoles: A Multidimensional Workload-Aware Histogram 2001 SIGMOD 0.00020041735
388 Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors 2009 VLDB 0.00019410042
401 Deep Unsupervised Cardinality Estimation 2020 VLDB 0.00019092557
448 Self-tuning Histograms: Building Histograms Without Looking at Data 1999 SIGMOD 0.00018292618
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
692 Independence is Good: Dependency-Based Histogram Synopses for High-Dimensional Data 2001 SIGMOD 0.00014919816
694 Cardinality Estimation Done Right: Index-Based Join Sampling 2017 CIDR 0.00014911698
697 Selectivity Estimation for Range Predicates using Lightweight Models 2019 VLDB 0.00014888851
723 Dynamic Multidimensional Histograms 2002 SIGMOD 0.00014620977
802 Random Sampling over Joins Revisited 2018 SIGMOD 0.00013907725
850 Approximating Multi-Dimensional Aggregate Range Queries Over Real Attributes 2000 SIGMOD 0.00013619394
934 Selectivity Estimation and Query Optimization in Large Databases with Highly Skewed Distributions of Column Values 1988 VLDB 0.00013108714
1,071 Lightweight Graphical Models for Selectivity Estimation Without Independence Assumptions 2011 VLDB 0.00012322342
1,170 QuickSel: Quick Selectivity Learning with Mixture Models 2020 SIGMOD 0.00011827259
1,440 On the Relative Cost of Sampling for Join Selectivity Estimation 1994 PODS 0.00010778889
1,468 Towards a Learning Optimizer for Shared Clouds 2019 VLDB 0.00010686496
1,499 Pessimistic Cardinality Estimation: Tighter Upper Bounds for Intermediate Join Cardinalities 2019 SIGMOD 0.00010564536
1,503 Self-Tuning, GPU-Accelerated Kernel Density Models for Multidimensional Selectivity Estimation 2015 SIGMOD 0.000105564
1,573 Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries 2020 SIGMOD 0.00010328171
1,668 Global Optimization of Histograms 2001 SIGMOD 0.00010057026
2,121 SASH: A Self-Adaptive Histogram Set for Dynamically Changing Workloads 2003 VLDB 9.1402718e-05
2,203 Estimating Join Selectivities using Bandwidth-Optimized Kernel Density Models 2017 VLDB 8.9610447e-05
2,369 Data Generation using Declarative Constraints 2011 SIGMOD 8.682429e-05
2,984 Multiple Join Size Estimation by Virtual Domains (extended abstract) 1993 PODS 7.8920597e-05
3,788 Graph-Based Synopses for Relational Selectivity Estimation 2006 SIGMOD 7.1244416e-05
3,994 Generating Databases for Query Workloads 2010 VLDB 6.9686731e-05
5,743 Joins on Samples: A Theoretical Guide for Practitioners 2020 VLDB 6.1025457e-05
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