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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
hd81e2623697a76e2
Venue
SIGMOD
Year
2019
Pagerank
7.4744941e-05
Overall Rank
3,271 | 78.01%
DOI
10.1145/3299869.3320218

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{kipf_sigmod19,
        title = {{Estimating Cardinalities with Deep Sketches}},
        author = {Kipf, Andreas and Vorona, Dimitri and Müller, Jonas and Kipf, Thomas and Radke, Bernhard and Leis, Viktor and Boncz, Peter and Neumann, Thomas and Kemper, Alfons},
        series = {{SIGMOD} '19},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3299869.3320218},
        url = {https://dl.acm.org/doi/10.1145/3299869.3320218},
        year = {2019}
}

Incoming Citations (Sorted by Pagerank)

Showing 17 of 17 citing papers.

Rank Citing Paper Year Venue Pagerank
1,580 Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries 2020 SIGMOD 0.00010180835
1,734 Flow-Loss: Learning Cardinality Estimates That Matter 2021 VLDB 9.7545773e-05
2,583 Fauce: Fast and Accurate Deep Ensembles with Uncertainty for Cardinality Estimation 2021 VLDB 8.2589758e-05
3,327 Robust Query Driven Cardinality Estimation under Changing Workloads 2023 VLDB 7.4207879e-05
3,590 Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation 2021 VLDB 7.1865343e-05
5,003 COMPASS: Online Sketch-based Query Optimization for In-Memory Databases 2021 SIGMOD 6.3188773e-05
5,241 FASTgres: Making Learned Query Optimizer Hinting Effective 2023 VLDB 6.2154384e-05
6,141 Expand your Training Limits! Generating Training Data for ML-based Data Management 2021 SIGMOD 5.8733296e-05
7,460 T3: Accurate and Fast Performance Prediction for Relational Database Systems With Compiled Decision Trees 2025 SIGMOD 5.5215755e-05
7,915 Tastes Great! Less Filling! High Performance and Accurate Training Data Collection for Self-Driving Database Management Systems 2022 SIGMOD 5.4276987e-05
7,926 Design Trade-offs for a Robust Dynamic Hybrid Hash Join 2022 VLDB 5.425615e-05
8,164 Efficient Query Re-optimization with Judicious Subquery Selections 2023 SIGMOD 5.3852872e-05
8,477 TreeSensing: Linearly Compressing Sketches with Flexibility 2023 SIGMOD 5.3332727e-05
8,650 PostCENN: PostgreSQL with Machine Learning Models for Cardinality Estimation 2021 VLDB 5.2948655e-05
9,632 Small Selectivities Matter: Lifting the Burden of Empty Samples 2021 SIGMOD 5.1472849e-05
10,205 Turbo-Charging SPJ Query Plans with Learned Physical Join Operator Selections 2022 VLDB 5.0603873e-05
11,706 Regularized Pairwise Relationship based Analytics for Structured Data 2023 SIGMOD 4.9793485e-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.

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