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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
h82c92734a94745ed
Venue
SIGMOD
Year
2021
Pagerank
7.6828463e-05
Overall Rank
3,060 | 79.49%
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 35 of 35 citing papers.

Rank Citing Paper Year Venue Pagerank
983 Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation 2022 VLDB 0.00012677179
2,348 Learned Cardinality Estimation: An In-depth Study 2022 SIGMOD 8.5910072e-05
2,635 Neural Subgraph Counting with Wasserstein Estimator 2022 SIGMOD 8.1795762e-05
2,852 FactorJoin: A New Cardinality Estimation Framework for Join Queries 2023 SIGMOD 7.9201312e-05
3,269 Robust Query Driven Cardinality Estimation under Changing Workloads 2023 VLDB 7.463682e-05
4,315 ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads 2024 VLDB 6.6545412e-05
4,765 Machine Learning for Databases 2021 VLDB 6.4166895e-05
4,971 Debunking the Myth of Join Ordering: Toward Robust SQL Analytics 2025 SIGMOD 6.3209003e-05
5,043 Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing 2022 VLDB 6.28918e-05
5,075 SafeBound: A Practical System for Generating Cardinality Bounds 2023 SIGMOD 6.2721634e-05
5,237 SAM: Database Generation from Query Workloads with Supervised Autoregressive Models 2022 SIGMOD 6.2027113e-05
5,854 Modeling Shifting Workloads for Learned Database Systems 2024 SIGMOD 5.9520525e-05
6,672 Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation 2023 SIGMOD 5.6975871e-05
6,812 dbET: Execution Time Distribution-based Plan Selection 2023 SIGMOD 5.6597129e-05
6,840 ASM: Harmonizing Autoregressive Model, Sampling, and Multi-dimensional Statistics Merging for Cardinality Estimation 2024 SIGMOD 5.6513231e-05
7,377 PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost! 2021 VLDB 5.5238616e-05
8,026 A Spark Optimizer for Adaptive, Fine-Grained Parameter Tuning 2024 VLDB 5.3884724e-05
8,657 LIMAO: A Framework for Lifelong Modular Learned Query Optimization 2025 VLDB 5.2790784e-05
8,685 ShadowAQP: Efficient Approximate Group-by and Join Query via Attribute-oriented Sample Size Allocation and Data Generation 2023 VLDB 5.2730954e-05
8,908 Spatial Query Optimization With Learning 2024 VLDB 5.2369727e-05
9,240 NeuroSketch: Fast and Approximate Evaluation of Range Aggregate Queries with Neural Networks 2023 SIGMOD 5.1867235e-05
9,407 Efficient and Effective Cardinality Estimation for Skyline Family 2023 SIGMOD 5.1672232e-05
9,583 Athena: An Effective Learning-based Framework for Query Optimizer Performance Improvement 2025 SIGMOD 5.140977e-05
9,681 Are Joins over LSM-trees Ready? Take RocksDB as an Example 2025 VLDB 5.1258854e-05
9,753 A Practical Theory of Generalization in Selectivity Learning 2025 VLDB 5.1159926e-05
9,995 Graph Transformers for Query Plan Representation: Potentials and Challenges 2025 VLDB 5.0845476e-05
10,014 Selectivity Estimation for Queries Containing Predicates over Set-Valued Attributes 2023 SIGMOD 5.0838565e-05
10,143 Still Asking: How Good Are Query Optimizers, Really? 2025 VLDB 5.0597449e-05
10,257 PRICE: A Pretrained Model for Cross-Database Cardinality Estimation 2025 VLDB 5.039379e-05
10,327 Data-Agnostic Cardinality Learning from Imperfect Workloads 2025 VLDB 5.0241309e-05
10,370 An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL 2025 SIGMOD 5.0010514e-05
10,745 Qualitative Join Discovery in Data Lakes using Examples 2026 SIGMOD 4.9605343e-05
10,764 Practical Parameterized Query Optimization via Efficient Plan Reuse and List-wise Ranking 2026 SIGMOD 4.9605343e-05
10,873 BaCon: Efficient Batch Processing of Counting Queries 2026 VLDB 4.9605343e-05
11,321 ACE: A Cardinality Estimator for Set-Valued Queries 2025 VLDB 4.9605343e-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
15 How Good Are Query Optimizers, Really? 2016 VLDB 0.00061463175
37 Improved Histograms for Selectivity Estimation of Range Predicates 1996 SIGMOD 0.00047654188
79 Practical Selectivity Estimation through Adaptive Sampling 1990 SIGMOD 0.00036394222
84 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035920657
98 LEO - DB2's LEarning Optimizer 2001 VLDB 0.00034050614
160 CORDS: Automatic Discovery of Correlations and Soft Functional Dependencies 2004 SIGMOD 0.00027775994
287 Selectivity Estimation using Probabilistic Models 2001 SIGMOD 0.00022060616
320 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021128745
372 STHoles: A Multidimensional Workload-Aware Histogram 2001 SIGMOD 0.00019768921
381 Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors 2009 VLDB 0.00019497061
405 Deep Unsupervised Cardinality Estimation 2020 VLDB 0.00019020138
453 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.0001801482
457 Self-tuning Histograms: Building Histograms Without Looking at Data 1999 SIGMOD 0.00017915659
508 NeuroCard: One Cardinality Estimator for All Tables 2021 VLDB 0.00017108973
689 Cardinality Estimation Done Right: Index-Based Join Sampling 2017 CIDR 0.00014725904
690 Selectivity Estimation for Range Predicates using Lightweight Models 2019 VLDB 0.00014703258
701 Independence is Good: Dependency-Based Histogram Synopses for High-Dimensional Data 2001 SIGMOD 0.0001464136
745 Dynamic Multidimensional Histograms 2002 SIGMOD 0.00014267268
793 Random Sampling over Joins Revisited 2018 SIGMOD 0.00013903938
868 Approximating Multi-Dimensional Aggregate Range Queries Over Real Attributes 2000 SIGMOD 0.00013336675
960 Selectivity Estimation and Query Optimization in Large Databases with Highly Skewed Distributions of Column Values 1988 VLDB 0.00012825107
1,061 Lightweight Graphical Models for Selectivity Estimation Without Independence Assumptions 2011 VLDB 0.00012207625
1,156 QuickSel: Quick Selectivity Learning with Mixture Models 2020 SIGMOD 0.00011759683
1,436 Towards a Learning Optimizer for Shared Clouds 2019 VLDB 0.00010658836
1,444 Pessimistic Cardinality Estimation: Tighter Upper Bounds for Intermediate Join Cardinalities 2019 SIGMOD 0.00010616242
1,469 On the Relative Cost of Sampling for Join Selectivity Estimation 1994 PODS 0.00010533259
1,511 Self-Tuning, GPU-Accelerated Kernel Density Models for Multidimensional Selectivity Estimation 2015 SIGMOD 0.00010408003
1,583 Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries 2020 SIGMOD 0.00010148362
1,693 Global Optimization of Histograms 2001 SIGMOD 9.8377504e-05
2,154 SASH: A Self-Adaptive Histogram Set for Dynamically Changing Workloads 2003 VLDB 8.9382906e-05
2,228 Estimating Join Selectivities using Bandwidth-Optimized Kernel Density Models 2017 VLDB 8.7912743e-05
2,422 Data Generation using Declarative Constraints 2011 SIGMOD 8.4785012e-05
3,009 Multiple Join Size Estimation by Virtual Domains (extended abstract) 1993 PODS 7.7439468e-05
3,858 Graph-Based Synopses for Relational Selectivity Estimation 2006 SIGMOD 6.9543709e-05
4,113 Generating Databases for Query Workloads 2010 VLDB 6.7870932e-05
5,841 Joins on Samples: A Theoretical Guide for Practitioners 2020 VLDB 5.9569427e-05
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