| 2,506 |
Lero: A Learning-to-Rank Query Optimizer |
2023 |
VLDB |
8.5603022e-05 |
| 3,556 |
LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans |
2023 |
VLDB |
7.3766003e-05 |
| 3,755 |
Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift |
2023 |
SIGMOD |
7.2037909e-05 |
| 4,507 |
LEON: A New Framework for ML-Aided Query Optimization |
2023 |
VLDB |
6.723287e-05 |
| 4,581 |
Kepler: Robust Learning for Faster Parametric Query Optimization |
2023 |
SIGMOD |
6.6913226e-05 |
| 4,858 |
AutoSteer: Learned Query Optimization for Any SQL Database |
2023 |
VLDB |
6.5410955e-05 |
| 5,206 |
Stage: Query Execution Time Prediction in Amazon Redshift |
2024 |
SIGMOD |
6.3868465e-05 |
| 5,214 |
FASTgres: Making Learned Query Optimizer Hinting Effective |
2023 |
VLDB |
6.3840792e-05 |
| 5,503 |
Eraser: Eliminating Performance Regression on Learned Query Optimizer |
2024 |
VLDB |
6.2627904e-05 |
| 5,640 |
Sample-Efficient Cardinality Estimation Using Geometric Deep Learning |
2024 |
VLDB |
6.2029974e-05 |
| 6,282 |
How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks |
2025 |
SIGMOD |
5.9906069e-05 |
| 6,373 |
PilotScope: Steering Databases with Machine Learning Drivers |
2024 |
VLDB |
5.9616981e-05 |
| 6,433 |
Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective |
2024 |
VLDB |
5.9423732e-05 |
| 6,499 |
Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis |
2023 |
VLDB |
5.917181e-05 |
| 6,628 |
Join Order Selection with Deep Reinforcement Learning: Fundamentals, Techniques, and Challenges |
2023 |
VLDB |
5.8775266e-05 |
| 6,698 |
Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation |
2023 |
SIGMOD |
5.8569921e-05 |
| 6,888 |
Lemo: A Cache-Enhanced Learned Optimizer for Concurrent Queries |
2023 |
SIGMOD |
5.8154414e-05 |
| 7,261 |
E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model |
2025 |
VLDB |
5.715327e-05 |
| 7,269 |
Rethinking Learned Cost Models: Why Start from Scratch? |
2023 |
SIGMOD |
5.7128028e-05 |
| 7,722 |
The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions |
2024 |
VLDB |
5.6186987e-05 |
| 8,061 |
SlabCity: Whole-Query Optimization using Program Synthesis |
2023 |
VLDB |
5.5549338e-05 |
| 8,281 |
Can Large Language Models Be Query Optimizer for Relational Databases? |
2026 |
SIGMOD |
5.5167963e-05 |
| 8,463 |
Learned Offline Query Planning via Bayesian Optimization |
2025 |
SIGMOD |
5.4900503e-05 |
| 8,842 |
T3: Accurate and Fast Performance Prediction for Relational Database Systems With Compiled Decision Trees |
2025 |
SIGMOD |
5.4241294e-05 |
| 8,858 |
Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems |
2024 |
VLDB |
5.4212377e-05 |
| 8,988 |
BASE: Bridging the Gap between Cost and Latency for Query Optimization |
2023 |
VLDB |
5.4017106e-05 |
| 9,290 |
LIMAO: A Framework for Lifelong Modular Learned Query Optimization |
2025 |
VLDB |
5.3525489e-05 |
| 9,336 |
BladeDISC: Optimizing Dynamic Shape Machine Learning Workloads via Compiler Approach |
2023 |
SIGMOD |
5.3449422e-05 |
| 9,526 |
Low Rank Learning for Offline Query Optimization |
2025 |
SIGMOD |
5.3206369e-05 |
| 9,579 |
Approximate Sketches |
2024 |
SIGMOD |
5.3118431e-05 |
| 9,819 |
Athena: An Effective Learning-based Framework for Query Optimizer Performance Improvement |
2025 |
SIGMOD |
5.2648912e-05 |
| 9,959 |
An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL |
2025 |
SIGMOD |
5.2142386e-05 |
| 10,018 |
GenJoin: Conditional Generative Plan-to-Plan Query Optimizer that Learns from Subplan Hints |
2026 |
SIGMOD |
5.1725247e-05 |
| 10,032 |
Rainbow: Risk-aware Index Benefit Estimation Facing Out Of Distribution Workloads |
2026 |
SIGMOD |
5.1725247e-05 |
| 10,050 |
APQO: An Adaptive Framework for Parametric Query Optimization |
2026 |
SIGMOD |
5.1725247e-05 |
| 10,096 |
NeuSO: Neural Optimizer for Subgraph Queries |
2026 |
SIGMOD |
5.1725247e-05 |
| 10,112 |
SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer |
2026 |
SIGMOD |
5.1725247e-05 |
| 10,156 |
Divo: Learning a Stable and Effective Query Optimizer with a Diverse Workload |
2026 |
SIGMOD |
5.1725247e-05 |
| 10,219 |
Practical Parameterized Query Optimization via Efficient Plan Reuse and List-wise Ranking |
2026 |
SIGMOD |
5.1725247e-05 |
| 10,225 |
LIO: A lightweight and interpretable query optimizer based on an evolutionary forest |
2026 |
VLDB |
5.1725247e-05 |
| 10,227 |
Sample-based Distinct Cardinality Estimation for Multiple Attributes in Multi-Dataset Queries |
2026 |
VLDB |
5.1725247e-05 |
| 10,271 |
OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning |
2026 |
VLDB |
5.1725247e-05 |
| 10,282 |
Toward Drift-Aware Database Benchmarking |
2026 |
VLDB |
5.1725247e-05 |
| 10,300 |
TATA: An Efficient Framework for Task Transfer in Query Plan Representation |
2026 |
VLDB |
5.1725247e-05 |
| 10,635 |
Robust Plan Evaluation based on Approximate Probabilistic Machine Learning |
2025 |
VLDB |
5.1725247e-05 |
| 10,638 |
Conformal Prediction for Verifiable Learned Query Optimization |
2025 |
VLDB |
5.1725247e-05 |
| 10,733 |
Improving DBMS Scheduling Decisions with Accurate Performance Prediction on Concurrent Queries |
2025 |
VLDB |
5.1725247e-05 |
| 10,778 |
veDB-HTAP: a Highly Integrated, Efficient and Adaptive HTAP System |
2025 |
VLDB |
5.1725247e-05 |
| 10,844 |
Learned Cost Models for Query Optimization: From Batch to Streaming Systems |
2025 |
VLDB |
5.1725247e-05 |
| 10,863 |
Graph Transformers for Query Plan Representation: Potentials and Challenges |
2025 |
VLDB |
5.1725247e-05 |