DBScholar

Back to papers

Rethinking Learned Cost Models: Why Start from Scratch?

Summary: Tuning the cost model by identifying key parameters and using a fast-learning adjuster per hardware/software config. Dynamic partitioning of the config space refines estimates from rough to fine, enabling transferable performance across DBMS instances. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6837
Venue
SIGMOD
Year
2023
Pagerank
5.7388557e-05
Overall Rank
6,921 | 52.52%
DOI
10.1145/3626769

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{yang_sigmod23,
        title = {{Rethinking Learned Cost Models: Why Start from Scratch?}},
        author = {Yang, Jiani and Wu, Sai and Zhang, Dongxiang and Dai, Jian and Li, Feifei and Chen, Gang},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3626769},
        url = {https://dl.acm.org/doi/10.1145/3626769},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 8 of 8 citing papers.

Rank Citing Paper Year Venue Pagerank
6,088 How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks 2025 SIGMOD 5.9813965e-05
8,572 T3: Accurate and Fast Performance Prediction for Relational Database Systems With Compiled Decision Trees 2025 SIGMOD 5.4102362e-05
10,108 An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL 2025 SIGMOD 5.1347137e-05
10,230 Eliminating Redundant Feature Tests in Decision Tree and Random Forest Inference on SQL Predicates 2026 SIGMOD 5.093636e-05
10,518 Breaking the Isolation-Freshness Trade-off: Joint Adaptive Storage Optimization for HTAP Systems 2026 VLDB 5.093636e-05
10,559 OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning 2026 VLDB 5.093636e-05
11,065 Learned Cost Models for Query Optimization: From Batch to Streaming Systems 2025 VLDB 5.093636e-05
11,073 AXE: A Task Decomposition Approach to Learned LSM Tuning 2025 VLDB 5.093636e-05
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 25 of 25 cited papers.

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

Rank Cited Paper Year Venue Pagerank
1 Access Path Selection in a Relational Database Management System 1979 SIGMOD 0.0024089429
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
323 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021264788
347 Tuning Database Configuration Parameters with iTuned 2009 VLDB 0.00020651582
378 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00019638121
465 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.0001803934
563 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.0001650812
682 Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques 2012 VLDB 0.00015014887
1,241 Balsa: Learning a Query Optimizer Without Expert Demonstrations 2022 SIGMOD 0.00011521639
2,355 QueryFormer: A Tree Transformer Model for Query Plan Representation 2022 VLDB 8.7022189e-05
2,420 Lero: A Learning-to-Rank Query Optimizer 2023 VLDB 8.605257e-05
2,452 A Learned Query Rewrite System using Monte Carlo Tree Search 2022 VLDB 8.5584e-05
2,620 Fauce: Fast and Accurate Deep Ensembles with Uncertainty for Cardinality Estimation 2021 VLDB 8.3363963e-05
2,812 Query Performance Prediction for Concurrent Queries using Graph Embedding 2020 VLDB 8.0979597e-05
2,822 Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings 2020 SIGMOD 8.0898536e-05
2,844 Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction 2022 VLDB 8.0608767e-05
3,343 LlamaTune: Sample-Efficient DBMS Configuration Tuning 2022 VLDB 7.4983591e-05
3,587 HUNTER: An Online Cloud Database Hybrid Tuning System for Personalized Requirements 2022 SIGMOD 7.2829345e-05
3,953 Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload 2021 SIGMOD 6.996368e-05
4,671 PreQR: Pre-training Representation for SQL Understanding 2022 SIGMOD 6.5732787e-05
5,073 Database Workload Characterization with Query Plan Encoders 2022 VLDB 6.3751266e-05
6,024 Expand your Training Limits! Generating Training Data for ML-based Data Management 2021 SIGMOD 6.0031118e-05
6,031 Sia: Optimizing Queries using Learned Predicates 2021 SIGMOD 6.0007422e-05
Previous Page 1 / 1 Next

Semantically Similar Papers

# Overall Rank Paper Year Venue
1 6,357 A Unified Transferable Model for ML-Enhanced DBMS 2022 CIDR
2 5,340 Machine Learning for Databases 2021 VLDB
3 465 An End-to-End Learning-based Cost Estimator 2020 VLDB
4 11,023 Opening The Black-Box: Explaining Learned Cost Models For Databases 2025 VLDB
5 10,041 DBMS Fitting: Why should we learn what we already know? 2020 CIDR
6 11,065 Learned Cost Models for Query Optimization: From Batch to Streaming Systems 2025 VLDB
7 4,468 One Model to Rule them All: Towards Zero-Shot Learning for Databases 2022 CIDR
8 6,323 Modeling Shifting Workloads for Learned Database Systems 2024 SIGMOD
9 6,088 How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks 2025 SIGMOD
10 2,844 Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction 2022 VLDB