Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction

Summary: Zero-shot cost models deliver out-of-the-box learned cost estimation that generalizes to unseen databases without training queries. A novel architecture and workload encoding enable transfer from pre-trained models, outperforming workload-driven baselines and supporting few-shot refinement on new databases. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 12728
Venue: VLDB
Year: 2022
Pagerank: 6.7208524e-05
Overall Rank: 3,828 | 73.38%
DOI: 10.14778/3551793.3551799

Incoming Non-self Citations Over Time

Authors

1. Benjamin Hilprecht
2. Carsten Binnig

Incoming Citations (Sorted by Pagerank)

Showing 32 of 32 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
1,082	CAESURA: Language Models as Multi-Modal Query Planners	2024	CIDR	0.00014214232
4,593	Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift	2023	SIGMOD	6.0606891e-05
5,832	Stage: Query Execution Time Prediction in Amazon Redshift	2024	SIGMOD	5.3111109e-05
6,383	Sample-Efficient Cardinality Estimation Using Geometric Deep Learning	2024	VLDB	5.0884322e-05
6,685	How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks	2025	SIGMOD	4.9627485e-05
7,753	Rethinking Learned Cost Models: Why Start from Scratch?	2023	SIGMOD	4.660151e-05
7,990	Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRAD	2024	VLDB	4.6117441e-05
8,659	Learned Offline Query Planning via Bayesian Optimization	2025	SIGMOD	4.4722928e-05
8,834	ByteCard: Enhancing ByteDance’s Data Warehouse with Learned Cardinality Estimation	2024	SIGMOD	4.4394021e-05
8,847	Towards Foundation Database Models	2025	CIDR	4.4371897e-05
8,854	Optimizing the cloud? Don't train models. Build oracles!	2024	CIDR	4.4349047e-05
8,956	T3: Accurate and Fast Performance Prediction for Relational Database Systems With Compiled Decision Trees	2025	SIGMOD	4.4214154e-05
9,187	POLAR: Adaptive and Non-invasive Join Order Selection via Plans of Least Resistance	2024	VLDB	4.3780059e-05
9,345	LIMAO: A Framework for Lifelong Modular Learned Query Optimization	2025	VLDB	4.3536343e-05
9,587	Low Rank Learning for Offline Query Optimization	2025	SIGMOD	4.3215645e-05
9,878	PRICE: A Pretrained Model for Cross-Database Cardinality Estimation	2025	VLDB	4.2656547e-05
9,917	Check Out the Big Brain on BRAD: Simplifying Cloud Data Processing with Learned Automated Data Meshes	2023	VLDB	4.2561557e-05
9,930	Wii: Dynamic Budget Reallocation In Index Tuning	2024	SIGMOD	4.2510122e-05
9,960	An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL	2025	SIGMOD	4.2294678e-05
10,018	GenJoin: Conditional Generative Plan-to-Plan Query Optimizer that Learns from Subplan Hints	2026	SIGMOD	4.1945683e-05
10,125	Understanding and Detecting Query Performance Regression in Practical Index Tuning: [Experiments & Analysis]	2026	SIGMOD	4.1945683e-05
10,203	Reqo: A Comprehensive Learning-Based Cost Model for Robust and Explainable Query Optimization	2026	SIGMOD	4.1945683e-05
10,217	This is Going to Sound Crazy, But What If We Used Large Language Models to Boost Automatic Database Tuning Algorithms By Leveraging Prior History? We Will Find Better Configurations More Quickly Than Retraining From Scratch!	2026	SIGMOD	4.1945683e-05
10,265	AQD: Online Adaptive Query Dispatcher for HTAP Databases	2026	VLDB	4.1945683e-05
10,271	OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning	2026	VLDB	4.1945683e-05
10,328	Libra: One-Shot Parameter Sensitivity Estimation for Transfer Learning in Database Performance Prediction	2026	VLDB	4.1945683e-05
10,543	Esc: An Early-Stopping Checker for Budget-aware Index Tuning	2025	VLDB	4.1945683e-05
10,726	Improving DBMS Scheduling Decisions with Accurate Performance Prediction on Concurrent Queries	2025	VLDB	4.1945683e-05
10,795	Opening The Black-Box: Explaining Learned Cost Models For Databases	2025	VLDB	4.1945683e-05
10,840	Learned Cost Models for Query Optimization: From Batch to Streaming Systems	2025	VLDB	4.1945683e-05
10,859	Graph Transformers for Query Plan Representation: Potentials and Challenges	2025	VLDB	4.1945683e-05
11,084	Presto’s History-based Query Optimizer	2024	VLDB	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

Showing 17 of 17 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
204	Learned Cardinalities: Estimating Correlated Joins with Deep Learning	2019	CIDR	0.00034784455
333	Neo: A Learned Query Optimizer	2019	VLDB	0.00027206884
514	An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning	2019	SIGMOD	0.0002124895
608	DeepDB: Learn from Data, not from Queries!	2020	VLDB	0.00019235898
640	Bao: Making Learned Query Optimization Practical	2021	SIGMOD	0.00018759152
659	The Making of TPC-DS	2006	VLDB	0.00018500853
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
910	NeuroCard: One Cardinality Estimator for All Tables	2021	VLDB	0.00015423056
1,019	Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques	2012	VLDB	0.00014625603
2,083	Towards a Learning Optimizer for Shared Clouds	2019	VLDB	9.5834572e-05
3,076	Learning a Partitioning Advisor for Cloud Databases	2020	SIGMOD	7.6107677e-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
4,804	Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload	2021	SIGMOD	5.910467e-05
6,519	Expand your Training Limits! Generating Training Data for ML-based Data Management	2021	SIGMOD	5.0316686e-05
9,892	DBMS Fitting: Why should we learn what we already know?	2020	CIDR	4.261445e-05

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