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DBMS Fitting: Why should we learn what we already know?

Summary: Critiques end-to-end DNN replacements for DBMS components as data-hungry and opaque; proposes leveraging differentiable programming to fit components using known algorithmic structure rather than learning behavior from scratch. Case study: fitted cost model for query plans with initial promising results. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
370
Venue
CIDR
Year
2020
Pagerank
5.1709251e-05
Overall Rank
10,041 | 31.11%
DOI
-

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{hilprecht_cidr20,
        address = {Amsterdam, Netherlands},
        series = {{CIDR} '20},
        title = {{DBMS Fitting: Why should we learn what we already know?}},
        booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
        author = {Hilprecht, Benjamin and Bang, Tiemo and El-Hindi, Muhammad and Hättasch, Benjamin and Khanna, Aditya and Rehrmann, Robin and Röhm, Uwe and Schmidt, Andreas and Thostrup, Lasse and Ziegler, Tobias and Binnig, Carsten},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
2,844 Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction 2022 VLDB 8.0608767e-05
4,468 One Model to Rule them All: Towards Zero-Shot Learning for Databases 2022 CIDR 6.6819041e-05
8,572 T3: Accurate and Fast Performance Prediction for Relational Database Systems With Compiled Decision Trees 2025 SIGMOD 5.4102362e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 13 of 13 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
84 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035838391
86 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00035316107
154 Neo: A Learned Query Optimizer 2019 VLDB 0.00028726181
334 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD 0.00020875082
447 ALEX: An Updatable Adaptive Learned Index 2020 SIGMOD 0.00018322593
461 Query-based Workload Forecasting for Self-Driving Database Management Systems 2018 SIGMOD 0.00018068441
465 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.0001803934
568 SageDB: A Learned Database System 2019 CIDR 0.0001641553
790 FITing-Tree: A Data-aware Index Structure 2019 SIGMOD 0.0001401445
1,799 DBEst: Revisiting Approximate Query Processing Engines with Machine Learning Models 2019 SIGMOD 9.7326398e-05
1,942 Design Continuums and the Path Toward Self-Designing Key-Value Stores that Know and Learn 2019 CIDR 9.4451535e-05
Previous Page 1 / 1 Next

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