| 20 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00058294381 |
| 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 |
| 157 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00028782395 |
| 328 |
DeepDB: Learn from Data, not from Queries! |
2020 |
VLDB |
0.00021121613 |
| 342 |
Tuning Database Configuration Parameters with iTuned |
2009 |
VLDB |
0.00020771604 |
| 387 |
Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors |
2009 |
VLDB |
0.00019538003 |
| 394 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00019400224 |
| 411 |
Deep Unsupervised Cardinality Estimation |
2020 |
VLDB |
0.00019014394 |
| 462 |
An End-to-End Learning-based Cost Estimator |
2020 |
VLDB |
0.0001813892 |
| 524 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00017177356 |
| 560 |
Plan-Structured Deep Neural Network Models for Query Performance Prediction |
2019 |
VLDB |
0.00016570735 |
| 702 |
Cardinality Estimation Done Right: Index-Based Join Sampling |
2017 |
CIDR |
0.00014941084 |
| 1,280 |
AI Meets AI: Leveraging Query Executions to Improve Index Recommendations |
2019 |
SIGMOD |
0.0001144289 |
| 1,335 |
DB-BERT: A Database Tuning Tool that "Reads the Manual" |
2022 |
SIGMOD |
0.00011217342 |
| 1,371 |
Balsa: Learning a Query Optimizer Without Expert Demonstrations |
2022 |
SIGMOD |
0.00011100806 |
| 1,468 |
Towards a Learning Optimizer for Shared Clouds |
2019 |
VLDB |
0.00010750589 |
| 1,606 |
Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries |
2020 |
SIGMOD |
0.00010308107 |
| 1,931 |
Flow-Loss: Learning Cardinality Estimates That Matter |
2021 |
VLDB |
9.5483519e-05 |
| 1,976 |
FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation |
2021 |
VLDB |
9.4645971e-05 |
| 2,323 |
Towards Cost-Optimal Query Processing in the Cloud |
2021 |
VLDB |
8.830919e-05 |
| 2,329 |
QueryFormer: A Tree Transformer Model for Query Plan Representation |
2022 |
VLDB |
8.8228164e-05 |
| 2,428 |
GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization |
2024 |
VLDB |
8.6581081e-05 |
| 2,506 |
Lero: A Learning-to-Rank Query Optimizer |
2023 |
VLDB |
8.5603022e-05 |
| 2,571 |
Why TPC Is Not Enough: An Analysis of the Amazon Redshift Fleet |
2024 |
VLDB |
8.4677877e-05 |
| 2,662 |
Fauce: Fast and Accurate Deep Ensembles with Uncertainty for Cardinality Estimation |
2021 |
VLDB |
8.344893e-05 |
| 2,780 |
Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings |
2020 |
SIGMOD |
8.1936279e-05 |
| 2,842 |
Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection |
2022 |
VLDB |
8.1092924e-05 |
| 2,929 |
Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction |
2022 |
VLDB |
8.0220847e-05 |
| 3,041 |
A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation |
2021 |
SIGMOD |
7.8821124e-05 |
| 3,161 |
ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases |
2021 |
SIGMOD |
7.7501079e-05 |
| 3,475 |
Robust Query Driven Cardinality Estimation under Changing Workloads |
2023 |
VLDB |
7.4606119e-05 |
| 3,556 |
LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans |
2023 |
VLDB |
7.3766003e-05 |
| 3,640 |
FACE: A Normalizing Flow based Cardinality Estimator |
2022 |
VLDB |
7.3040801e-05 |
| 3,963 |
Towards Dynamic and Safe Configuration Tuning for Cloud Databases |
2022 |
SIGMOD |
7.0613767e-05 |
| 4,507 |
LEON: A New Framework for ML-Aided Query Optimization |
2023 |
VLDB |
6.723287e-05 |
| 4,555 |
ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads |
2024 |
VLDB |
6.6989559e-05 |
| 4,781 |
LOCAT: Low-Overhead Online Configuration Auto-Tuning of Spark SQL Applications |
2022 |
SIGMOD |
6.5784829e-05 |
| 4,858 |
AutoSteer: Learned Query Optimization for Any SQL Database |
2023 |
VLDB |
6.5410955e-05 |
| 5,095 |
An Efficient Transfer Learning Based Configuration Adviser for Database Tuning |
2024 |
VLDB |
6.4313378e-05 |
| 5,206 |
Stage: Query Execution Time Prediction in Amazon Redshift |
2024 |
SIGMOD |
6.3868465e-05 |
| 5,366 |
Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing |
2022 |
VLDB |
6.3169865e-05 |
| 5,640 |
Sample-Efficient Cardinality Estimation Using Geometric Deep Learning |
2024 |
VLDB |
6.2029974e-05 |
| 5,696 |
A Comparative Study and Component Analysis of Query Plan Representation Techniques in ML4DB Studies |
2024 |
VLDB |
6.1834438e-05 |
| 6,247 |
Adaptive and Robust Query Execution for Lakehouses at Scale |
2024 |
VLDB |
6.0034176e-05 |
| 6,248 |
Towards General and Efficient Online Tuning for Spark |
2023 |
VLDB |
6.0022621e-05 |
| 7,074 |
Modeling Shifting Workloads for Learned Database Systems |
2024 |
SIGMOD |
5.7634614e-05 |
| 7,108 |
Weighted Distinct Sampling: Cardinality Estimation for SPJ Queries |
2021 |
SIGMOD |
5.7543545e-05 |
| 7,912 |
PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost! |
2021 |
VLDB |
5.587029e-05 |
| 8,472 |
A Spark Optimizer for Adaptive, Fine-Grained Parameter Tuning |
2024 |
VLDB |
5.4888877e-05 |