| 2,090 |
Balsa: Learning a Query Optimizer Without Expert Demonstrations |
2022 |
SIGMOD |
9.5668285e-05 |
| 2,937 |
DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems |
2021 |
VLDB |
7.8552033e-05 |
| 3,167 |
QueryFormer: A Tree Transformer Model for Query Plan Representation |
2022 |
VLDB |
7.4561078e-05 |
| 3,241 |
A Learned Query Rewrite System using Monte Carlo Tree Search |
2022 |
VLDB |
7.32744e-05 |
| 3,345 |
Lero: A Learning-to-Rank Query Optimizer |
2023 |
VLDB |
7.1908499e-05 |
| 3,435 |
Real-time Workload Pattern Analysis for Large-scale Cloud Databases |
2023 |
VLDB |
7.0946114e-05 |
| 3,729 |
Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection |
2022 |
VLDB |
6.8078013e-05 |
| 3,819 |
Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction |
2022 |
VLDB |
6.7267885e-05 |
| 4,056 |
Are Updatable Learned Indexes Ready? |
2022 |
VLDB |
6.4905689e-05 |
| 4,216 |
Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation |
2021 |
VLDB |
6.3448176e-05 |
| 4,382 |
HTAP Databases: What is New and What is Next |
2022 |
SIGMOD |
6.2318984e-05 |
| 4,413 |
Robust Query Driven Cardinality Estimation under Changing Workloads |
2023 |
VLDB |
6.1989918e-05 |
| 4,464 |
LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans |
2023 |
VLDB |
6.1552798e-05 |
| 4,592 |
Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift |
2023 |
SIGMOD |
6.056004e-05 |
| 4,687 |
Deploying a Steered Query Optimizer in Production at Microsoft |
2022 |
SIGMOD |
5.9915268e-05 |
| 5,323 |
Can Learned Models Replace Hash Functions? |
2023 |
VLDB |
5.5671086e-05 |
| 5,339 |
LEON: A New Framework for ML-Aided Query Optimization |
2023 |
VLDB |
5.5596755e-05 |
| 5,343 |
Learned Index Benefits: Machine Learning Based Index Performance Estimation |
2022 |
VLDB |
5.5582234e-05 |
| 5,412 |
Kepler: Robust Learning for Faster Parametric Query Optimization |
2023 |
SIGMOD |
5.5200608e-05 |
| 5,533 |
QueryBooster: Improving SQL Performance Using Middleware Services for Human-Centered Query Rewriting |
2023 |
VLDB |
5.4555519e-05 |
| 5,643 |
Intelligent Scaling in Amazon Redshift |
2024 |
SIGMOD |
5.3949759e-05 |
| 5,654 |
AutoSteer: Learned Query Optimization for Any SQL Database |
2023 |
VLDB |
5.3882121e-05 |
| 5,682 |
LSched: A Workload-Aware Learned Query Scheduler for Analytical Database Systems |
2022 |
SIGMOD |
5.3752251e-05 |
| 5,844 |
Stage: Query Execution Time Prediction in Amazon Redshift |
2024 |
SIGMOD |
5.3060581e-05 |
| 5,925 |
HMAB: Self-Driving Hierarchy of Bandits for Integrated Physical Database Design Tuning |
2023 |
VLDB |
5.2669029e-05 |
| 5,931 |
FASTgres: Making Learned Query Optimizer Hinting Effective |
2023 |
VLDB |
5.2632167e-05 |
| 5,941 |
Eraser: Eliminating Performance Regression on Learned Query Optimizer |
2024 |
VLDB |
5.2594013e-05 |
| 6,060 |
Efficient Massively Parallel Join Optimization for Large Queries* |
2022 |
SIGMOD |
5.2271244e-05 |
| 6,298 |
Towards instance-optimized data systems |
2021 |
VLDB |
5.1182917e-05 |
| 6,328 |
A Comparative Study and Component Analysis of Query Plan Representation Techniques in ML4DB Studies |
2024 |
VLDB |
5.1034426e-05 |
| 6,382 |
Sample-Efficient Cardinality Estimation Using Geometric Deep Learning |
2024 |
VLDB |
5.0835686e-05 |
| 6,639 |
Leveraging Query Logs and Machine Learning for Parametric Query Optimization |
2022 |
VLDB |
4.976781e-05 |
| 6,687 |
How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks |
2025 |
SIGMOD |
4.957987e-05 |
| 6,860 |
Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data |
2023 |
SIGMOD |
4.9008421e-05 |
| 6,862 |
Join Order Selection with Deep Reinforcement Learning: Fundamentals, Techniques, and Challenges |
2023 |
VLDB |
4.9004921e-05 |
| 6,883 |
PilotScope: Steering Databases with Machine Learning Drivers |
2024 |
VLDB |
4.8918682e-05 |
| 7,009 |
Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective |
2024 |
VLDB |
4.8597992e-05 |
| 7,011 |
Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis |
2023 |
VLDB |
4.8583284e-05 |
| 7,118 |
ASM: Harmonizing Autoregressive Model, Sampling, and Multi-dimensional Statistics Merging for Cardinality Estimation |
2024 |
SIGMOD |
4.8204951e-05 |
| 7,180 |
Coresets over Multiple Tables for Feature-rich and Data-efficient Machine Learning |
2023 |
VLDB |
4.8032775e-05 |
| 7,220 |
Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation |
2023 |
SIGMOD |
4.7926382e-05 |
| 7,326 |
Lemo: A Cache-Enhanced Learned Optimizer for Concurrent Queries |
2023 |
SIGMOD |
4.7563708e-05 |
| 7,332 |
Refactoring Index Tuning Process with Benefit Estimation |
2024 |
VLDB |
4.7553758e-05 |
| 7,486 |
Quantum-Inspired Digital Annealing for Join Ordering |
2024 |
VLDB |
4.7135369e-05 |
| 7,564 |
Modeling Shifting Workloads for Learned Database Systems |
2024 |
SIGMOD |
4.7049893e-05 |
| 7,676 |
E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model |
2025 |
VLDB |
4.6770108e-05 |
| 7,742 |
Rethinking Learned Cost Models: Why Start from Scratch? |
2023 |
SIGMOD |
4.6585812e-05 |
| 7,809 |
Sibyl: Forecasting Time-Evolving Query Workloads |
2024 |
SIGMOD |
4.6415559e-05 |
| 7,854 |
dbET: Execution Time Distribution-based Plan Selection |
2023 |
SIGMOD |
4.6306186e-05 |
| 7,887 |
SQLStorm: Taking Database Benchmarking into the LLM Era |
2025 |
VLDB |
4.6218382e-05 |