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Estimating Cardinalities with Deep Sketches

Summary: Deep Sketches are compact learned models for estimating SQL cardinalities that capture cross-column and cross-table correlations. Demonstrations on TPC-H and IMDb cover training, ad-hoc queries, and comparison to estimators on HyPer and PostgreSQL. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5726
Venue
SIGMOD
Year
2019
Pagerank
6.8170734e-05
Overall Rank
3,725 | 74.09%
DOI
10.1145/3299869.3320218

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 17 of 17 citing papers.

Rank Citing Paper Year Venue Pagerank
2,364 Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries 2020 SIGMOD 8.9554751e-05
2,783 Flow-Loss: Learning Cardinality Estimates That Matter 2021 VLDB 8.1293383e-05
3,499 Fauce: Fast and Accurate Deep Ensembles with Uncertainty for Cardinality Estimation 2021 VLDB 7.0376445e-05
4,240 Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation 2021 VLDB 6.3318228e-05
4,417 Robust Query Driven Cardinality Estimation under Changing Workloads 2023 VLDB 6.2037371e-05
5,880 COMPASS: Online Sketch-based Query Optimization for In-Memory Databases 2021 SIGMOD 5.2898074e-05
5,930 FASTgres: Making Learned Query Optimizer Hinting Effective 2023 VLDB 5.2682075e-05
6,519 Expand your Training Limits! Generating Training Data for ML-based Data Management 2021 SIGMOD 5.0316686e-05
8,023 Design Trade-offs for a Robust Dynamic Hybrid Hash Join 2022 VLDB 4.6035454e-05
8,082 Tastes Great! Less Filling! High Performance and Accurate Training Data Collection for Self-Driving Database Management Systems 2022 SIGMOD 4.5905454e-05
8,576 PostCENN: PostgreSQL with Machine Learning Models for Cardinality Estimation 2021 VLDB 4.4927989e-05
8,956 T3: Accurate and Fast Performance Prediction for Relational Database Systems With Compiled Decision Trees 2025 SIGMOD 4.4214154e-05
9,041 TreeSensing: Linearly Compressing Sketches with Flexibility 2023 SIGMOD 4.4039656e-05
9,380 Small Selectivities Matter: Lifting the Burden of Empty Samples 2021 SIGMOD 4.3461329e-05
9,662 Efficient Query Re-optimization with Judicious Subquery Selections 2023 SIGMOD 4.3097631e-05
9,869 Turbo-Charging SPJ Query Plans with Learned Physical Join Operator Selections 2022 VLDB 4.2675361e-05
11,187 Regularized Pairwise Relationship based Analytics for Structured Data 2023 SIGMOD 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 11 of 11 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
71 How Good Are Query Optimizers, Really? 2016 VLDB 0.00059038975
102 The Case for Learned Index Structures 2018 SIGMOD 0.00049545203
204 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00034784455
629 Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors 2009 VLDB 0.00018942366
801 SageDB: A Learned Database System 2019 CIDR 0.00016505496
884 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.00015654004
2,083 Towards a Learning Optimizer for Shared Clouds 2019 VLDB 9.5834572e-05
2,156 SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning 2018 VLDB 9.4170209e-05
2,865 Designing Succinct Secondary Indexing Mechanism by Exploiting Column Correlations 2019 SIGMOD 7.9862595e-05
3,658 Towards a Hands-Free Query Optimizer through Deep Learning 2019 CIDR 6.8704209e-05
4,549 Database-Agnostic Workload Management 2019 CIDR 6.0926728e-05
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