| 1 |
Access Path Selection in a Relational Database Management System |
1979 |
SIGMOD |
0.0024179717 |
| 20 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00058294381 |
| 86 |
Learned Cardinalities: Estimating Correlated Joins with Deep Learning |
2019 |
CIDR |
0.0003577267 |
| 157 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00028782395 |
| 328 |
DeepDB: Learn from Data, not from Queries! |
2020 |
VLDB |
0.00021121613 |
| 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 |
| 524 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00017177356 |
| 702 |
Cardinality Estimation Done Right: Index-Based Join Sampling |
2017 |
CIDR |
0.00014941084 |
| 715 |
Selectivity Estimation for Range Predicates using Lightweight Models |
2019 |
VLDB |
0.0001480646 |
| 975 |
Analyzing Plan Diagrams of Database Query Optimizers |
2005 |
VLDB |
0.0001295619 |
| 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 |
| 1,187 |
QuickSel: Quick Selectivity Learning with Mixture Models |
2020 |
SIGMOD |
0.00011849604 |
| 1,371 |
Balsa: Learning a Query Optimizer Without Expert Demonstrations |
2022 |
SIGMOD |
0.00011100806 |
| 1,542 |
Pessimistic Cardinality Estimation: Tighter Upper Bounds for Intermediate Join Cardinalities |
2019 |
SIGMOD |
0.00010485163 |
| 1,606 |
Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries |
2020 |
SIGMOD |
0.00010308107 |
| 1,646 |
The Picasso Database Query Optimizer Visualizer |
2010 |
VLDB |
0.00010197566 |
| 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 |
| 2,506 |
Lero: A Learning-to-Rank Query Optimizer |
2023 |
VLDB |
8.5603022e-05 |
| 2,533 |
Learned Cardinality Estimation: An In-depth Study |
2022 |
SIGMOD |
8.5233704e-05 |
| 2,662 |
Fauce: Fast and Accurate Deep Ensembles with Uncertainty for Cardinality Estimation |
2021 |
VLDB |
8.344893e-05 |
| 2,688 |
Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation |
2022 |
VLDB |
8.3172831e-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 |
| 3,108 |
FactorJoin: A New Cardinality Estimation Framework for Join Queries |
2023 |
SIGMOD |
7.8122622e-05 |
| 3,475 |
Robust Query Driven Cardinality Estimation under Changing Workloads |
2023 |
VLDB |
7.4606119e-05 |
| 3,525 |
Astrid: Accurate Selectivity Estimation for String Predicates using Deep Learning |
2021 |
VLDB |
7.4076098e-05 |
| 3,556 |
LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans |
2023 |
VLDB |
7.3766003e-05 |
| 3,877 |
Simplicity Done Right for Join Ordering |
2021 |
CIDR |
7.1096512e-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 |
| 4,986 |
Analyzing the Impact of Cardinality Estimation on Execution Plans in Microsoft SQL Server |
2023 |
VLDB |
6.4810456e-05 |
| 5,194 |
COMPASS: Online Sketch-based Query Optimization for In-Memory Databases |
2021 |
SIGMOD |
6.3919263e-05 |
| 5,214 |
FASTgres: Making Learned Query Optimizer Hinting Effective |
2023 |
VLDB |
6.3840792e-05 |
| 5,477 |
SafeBound: A Practical System for Generating Cardinality Bounds |
2023 |
SIGMOD |
6.2718956e-05 |
| 6,026 |
Leveraging Query Logs and Machine Learning for Parametric Query Optimization |
2022 |
VLDB |
6.0629003e-05 |
| 6,499 |
Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis |
2023 |
VLDB |
5.917181e-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 |
| 7,269 |
Rethinking Learned Cost Models: Why Start from Scratch? |
2023 |
SIGMOD |
5.7128028e-05 |
| 7,770 |
Efficiently Computing Join Orders with Heuristic Search |
2023 |
SIGMOD |
5.6064744e-05 |
| 8,466 |
A Study of Database Performance Sensitivity to Experiment Settings |
2022 |
VLDB |
5.4898469e-05 |
| 8,988 |
BASE: Bridging the Gap between Cost and Latency for Query Optimization |
2023 |
VLDB |
5.4017106e-05 |
| 9,579 |
Approximate Sketches |
2024 |
SIGMOD |
5.3118431e-05 |