| 22 |
SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets |
2008 |
VLDB |
0.0008456613 |
| 71 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00059038975 |
| 102 |
The Case for Learned Index Structures |
2018 |
SIGMOD |
0.00049545203 |
| 182 |
LEO - DB2's LEarning Optimizer |
2001 |
VLDB |
0.00036962631 |
| 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 |
| 220 |
Efficient Mid-Query Re-Optimization of Sub-Optimal Query Execution Plans |
1998 |
SIGMOD |
0.00033194808 |
| 237 |
An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server |
1997 |
VLDB |
0.00031726304 |
| 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 |
| 650 |
Robust Query Processing through Progressive Optimization |
2004 |
SIGMOD |
0.00018659177 |
| 826 |
ALEX: An Updatable Adaptive Learned Index |
2020 |
SIGMOD |
0.00016224841 |
| 910 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00015423056 |
| 1,017 |
Automatic Physical Database Tuning: A Relaxation-based Approach |
2005 |
SIGMOD |
0.00014634307 |
| 1,341 |
Dynamic Programming Strikes Back |
2008 |
SIGMOD |
0.00012486285 |
| 1,375 |
FITing-Tree: A Data-aware Index Structure |
2019 |
SIGMOD |
0.00012303141 |
| 1,638 |
Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation |
2022 |
VLDB |
0.00011049779 |
| 1,703 |
Are We Ready For Learned Cardinality Estimation? |
2021 |
VLDB |
0.00010836769 |
| 1,758 |
Sampling-Based Query Re-Optimization |
2016 |
SIGMOD |
0.00010655546 |
| 1,855 |
AI Meets AI: Leveraging Query Executions to Improve Index Recommendations |
2019 |
SIGMOD |
0.00010315245 |
| 2,020 |
Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms |
2020 |
VLDB |
9.762624e-05 |
| 2,121 |
Balsa: Learning a Query Optimizer Without Expert Demonstrations |
2022 |
SIGMOD |
9.5017232e-05 |
| 2,139 |
Diagnosing Root Causes of Intermittent Slow Queries in Cloud Databases |
2020 |
VLDB |
9.4640037e-05 |
| 2,219 |
SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning |
2019 |
SIGMOD |
9.2623533e-05 |
| 2,470 |
CoPhy: A Scalable, Portable, and Interactive Index Advisor for Large Workloads |
2011 |
VLDB |
8.7333019e-05 |
| 2,762 |
FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation |
2021 |
VLDB |
8.1585394e-05 |
| 3,348 |
Lero: A Learning-to-Rank Query Optimizer |
2023 |
VLDB |
7.1904529e-05 |
| 3,449 |
Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation |
2022 |
VLDB |
7.0824319e-05 |
| 3,473 |
AI Meets Database: AI4DB and DB4AI |
2021 |
SIGMOD |
7.062864e-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 |
| 3,727 |
Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection |
2022 |
VLDB |
6.8141709e-05 |
| 3,812 |
Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation |
2022 |
VLDB |
6.7373184e-05 |
| 4,152 |
openGauss: An Autonomous Database System |
2021 |
VLDB |
6.4060406e-05 |
| 4,174 |
Computation Reuse in Analytics Job Service at Microsoft |
2018 |
SIGMOD |
6.3856219e-05 |
| 4,239 |
The Composable Data Management System Manifesto |
2023 |
VLDB |
6.3318452e-05 |
| 4,240 |
Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation |
2021 |
VLDB |
6.3318228e-05 |
| 4,380 |
LlamaTune: Sample-Efficient DBMS Configuration Tuning |
2022 |
VLDB |
6.2396606e-05 |
| 4,590 |
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems |
2021 |
SIGMOD |
6.0620053e-05 |
| 4,690 |
Deploying a Steered Query Optimizer in Production at Microsoft |
2022 |
SIGMOD |
5.997226e-05 |
| 4,804 |
Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload |
2021 |
SIGMOD |
5.910467e-05 |
| 5,334 |
LEON: A New Framework for ML-Aided Query Optimization |
2023 |
VLDB |
5.5649836e-05 |
| 5,640 |
AutoSteer: Learned Query Optimization for Any SQL Database |
2023 |
VLDB |
5.3933314e-05 |
| 5,952 |
Eraser: Eliminating Performance Regression on Learned Query Optimizer |
2024 |
VLDB |
5.2591691e-05 |
| 6,040 |
Steering Query Optimizers: A Practical Take on Big Data Workloads |
2021 |
SIGMOD |
5.2412035e-05 |
| 6,874 |
ROX: Run-time Optimization of XQueries |
2009 |
SIGMOD |
4.8978984e-05 |
| 7,610 |
Learning to be a Statistician: Learned Estimator for Number of Distinct Values |
2022 |
VLDB |
4.6965039e-05 |
| 8,082 |
Tastes Great! Less Filling! High Performance and Accurate Training Data Collection for Self-Driving Database Management Systems |
2022 |
SIGMOD |
4.5905454e-05 |