| 20 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00058294381 |
| 31 |
SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets |
2008 |
VLDB |
0.00051798124 |
| 45 |
The Case for Learned Index Structures |
2018 |
SIGMOD |
0.0004530684 |
| 86 |
Learned Cardinalities: Estimating Correlated Joins with Deep Learning |
2019 |
CIDR |
0.0003577267 |
| 89 |
Automatic Database Management System Tuning Through Large-scale Machine Learning |
2017 |
SIGMOD |
0.00035598024 |
| 99 |
LEO - DB2's LEarning Optimizer |
2001 |
VLDB |
0.00034651834 |
| 150 |
Efficient Mid-Query Re-Optimization of Sub-Optimal Query Execution Plans |
1998 |
SIGMOD |
0.00029370827 |
| 154 |
An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server |
1997 |
VLDB |
0.00028960058 |
| 157 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00028782395 |
| 328 |
DeepDB: Learn from Data, not from Queries! |
2020 |
VLDB |
0.00021121613 |
| 334 |
An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning |
2019 |
SIGMOD |
0.00020961385 |
| 394 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00019400224 |
| 485 |
ALEX: An Updatable Adaptive Learned Index |
2020 |
SIGMOD |
0.00017714392 |
| 491 |
Robust Query Processing through Progressive Optimization |
2004 |
SIGMOD |
0.00017645668 |
| 524 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00017177356 |
| 755 |
Automatic Physical Database Tuning: A Relaxation-based Approach |
2005 |
SIGMOD |
0.00014368592 |
| 838 |
FITing-Tree: A Data-aware Index Structure |
2019 |
SIGMOD |
0.0001375596 |
| 1,053 |
Are We Ready For Learned Cardinality Estimation? |
2021 |
VLDB |
0.00012504851 |
| 1,074 |
Dynamic Programming Strikes Back |
2008 |
SIGMOD |
0.00012416314 |
| 1,164 |
Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation |
2022 |
VLDB |
0.00011978719 |
| 1,250 |
Sampling-Based Query Re-Optimization |
2016 |
SIGMOD |
0.00011563119 |
| 1,280 |
AI Meets AI: Leveraging Query Executions to Improve Index Recommendations |
2019 |
SIGMOD |
0.0001144289 |
| 1,371 |
Balsa: Learning a Query Optimizer Without Expert Demonstrations |
2022 |
SIGMOD |
0.00011100806 |
| 1,481 |
Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms |
2020 |
VLDB |
0.00010699268 |
| 1,711 |
SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning |
2019 |
SIGMOD |
0.00010036767 |
| 1,883 |
CoPhy: A Scalable, Portable, and Interactive Index Advisor for Large Workloads |
2011 |
VLDB |
9.6396398e-05 |
| 1,932 |
Diagnosing Root Causes of Intermittent Slow Queries in Cloud Databases |
2020 |
VLDB |
9.5370326e-05 |
| 1,976 |
FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation |
2021 |
VLDB |
9.4645971e-05 |
| 2,506 |
Lero: A Learning-to-Rank Query Optimizer |
2023 |
VLDB |
8.5603022e-05 |
| 2,688 |
Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation |
2022 |
VLDB |
8.3172831e-05 |
| 2,780 |
Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings |
2020 |
SIGMOD |
8.1936279e-05 |
| 2,781 |
Query Performance Prediction for Concurrent Queries using Graph Embedding |
2020 |
VLDB |
8.1921284e-05 |
| 2,842 |
Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection |
2022 |
VLDB |
8.1092924e-05 |
| 2,850 |
AI Meets Database: AI4DB and DB4AI |
2021 |
SIGMOD |
8.1022388e-05 |
| 2,859 |
Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation |
2022 |
VLDB |
8.094221e-05 |
| 3,298 |
LlamaTune: Sample-Efficient DBMS Configuration Tuning |
2022 |
VLDB |
7.6094798e-05 |
| 3,531 |
The Composable Data Management System Manifesto |
2023 |
VLDB |
7.3965633e-05 |
| 3,555 |
Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation |
2021 |
VLDB |
7.3766884e-05 |
| 3,560 |
Computation Reuse in Analytics Job Service at Microsoft |
2018 |
SIGMOD |
7.3715168e-05 |
| 3,632 |
openGauss: An Autonomous Database System |
2021 |
VLDB |
7.3121856e-05 |
| 3,911 |
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems |
2021 |
SIGMOD |
7.0870659e-05 |
| 3,913 |
Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload |
2021 |
SIGMOD |
7.0855169e-05 |
| 3,940 |
Deploying a Steered Query Optimizer in Production at Microsoft |
2022 |
SIGMOD |
7.0722403e-05 |
| 4,507 |
LEON: A New Framework for ML-Aided Query Optimization |
2023 |
VLDB |
6.723287e-05 |
| 4,858 |
AutoSteer: Learned Query Optimization for Any SQL Database |
2023 |
VLDB |
6.5410955e-05 |
| 5,401 |
Steering Query Optimizers: A Practical Take on Big Data Workloads |
2021 |
SIGMOD |
6.30051e-05 |
| 5,503 |
Eraser: Eliminating Performance Regression on Learned Query Optimizer |
2024 |
VLDB |
6.2627904e-05 |
| 6,616 |
ROX: Run-time Optimization of XQueries |
2009 |
SIGMOD |
5.8812707e-05 |
| 7,175 |
Learning to be a Statistician: Learned Estimator for Number of Distinct Values |
2022 |
VLDB |
5.73923e-05 |
| 7,664 |
Tastes Great! Less Filling! High Performance and Accurate Training Data Collection for Self-Driving Database Management Systems |
2022 |
SIGMOD |
5.6285439e-05 |