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: 6775
Venue: SIGMOD
Year: 2023
Pagerank: 4.660151e-05
Overall Rank: 7,753 | 46.07%
DOI: 10.1145/3626769

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 7 of 7 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
6,685	How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks	2025	SIGMOD	4.9627485e-05
8,956	T3: Accurate and Fast Performance Prediction for Relational Database Systems With Compiled Decision Trees	2025	SIGMOD	4.4214154e-05
9,960	An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL	2025	SIGMOD	4.2294678e-05
10,230	Breaking the Isolation-Freshness Trade-off: Joint Adaptive Storage Optimization for HTAP Systems	2026	VLDB	4.1945683e-05
10,271	OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning	2026	VLDB	4.1945683e-05
10,840	Learned Cost Models for Query Optimization: From Batch to Streaming Systems	2025	VLDB	4.1945683e-05
10,849	AXE: A Task Decomposition Approach to Learned LSM Tuning	2025	VLDB	4.1945683e-05

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	0.0040449103
183	Automatic Database Management System Tuning Through Large-scale Machine Learning	2017	SIGMOD	0.00036721403
204	Learned Cardinalities: Estimating Correlated Joins with Deep Learning	2019	CIDR	0.00034784455
333	Neo: A Learned Query Optimizer	2019	VLDB	0.00027206884
424	Tuning Database Configuration Parameters with iTuned	2009	VLDB	0.00023616398
608	DeepDB: Learn from Data, not from Queries!	2020	VLDB	0.00019235898
640	Bao: Making Learned Query Optimization Practical	2021	SIGMOD	0.00018759152
806	An End-to-End Learning-based Cost Estimator	2020	VLDB	0.00016434274
884	Plan-Structured Deep Neural Network Models for Query Performance Prediction	2019	VLDB	0.00015654004
1,019	Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques	2012	VLDB	0.00014625603
2,121	Balsa: Learning a Query Optimizer Without Expert Demonstrations	2022	SIGMOD	9.5017232e-05
3,169	QueryFormer: A Tree Transformer Model for Query Plan Representation	2022	VLDB	7.4498425e-05
3,248	A Learned Query Rewrite System using Monte Carlo Tree Search	2022	VLDB	7.3258782e-05
3,348	Lero: A Learning-to-Rank Query Optimizer	2023	VLDB	7.1904529e-05
3,499	Fauce: Fast and Accurate Deep Ensembles with Uncertainty for Cardinality Estimation	2021	VLDB	7.0376445e-05
3,580	Query Performance Prediction for Concurrent Queries using Graph Embedding	2020	VLDB	6.9500996e-05
3,625	Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings	2020	SIGMOD	6.9055212e-05
3,828	Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction	2022	VLDB	6.7208524e-05
4,380	LlamaTune: Sample-Efficient DBMS Configuration Tuning	2022	VLDB	6.2396606e-05
4,399	HUNTER: An Online Cloud Database Hybrid Tuning System for Personalized Requirements	2022	SIGMOD	6.2225151e-05
4,661	PreQR: Pre-training Representation for SQL Understanding	2022	SIGMOD	6.0137947e-05
4,804	Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload	2021	SIGMOD	5.910467e-05
5,637	Database Workload Characterization with Query Plan Encoders	2022	VLDB	5.3979505e-05
6,519	Expand your Training Limits! Generating Training Data for ML-based Data Management	2021	SIGMOD	5.0316686e-05
7,283	Sia: Optimizing Queries using Learned Predicates	2021	SIGMOD	4.7764688e-05

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