| 1 |
Access Path Selection in a Relational Database Management System |
1979 |
SIGMOD |
0.0040449103 |
| 71 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00059038975 |
| 204 |
Learned Cardinalities: Estimating Correlated Joins with Deep Learning |
2019 |
CIDR |
0.00034784455 |
| 333 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00027206884 |
| 608 |
DeepDB: Learn from Data, not from Queries! |
2020 |
VLDB |
0.00019235898 |
| 629 |
Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors |
2009 |
VLDB |
0.00018942366 |
| 640 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00018759152 |
| 758 |
Deep Unsupervised Cardinality Estimation |
2020 |
VLDB |
0.0001706608 |
| 910 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00015423056 |
| 1,070 |
Analyzing Plan Diagrams of Database Query Optimizers |
2005 |
VLDB |
0.00014316791 |
| 1,105 |
Cardinality Estimation Done Right: Index-Based Join Sampling |
2017 |
CIDR |
0.00013990395 |
| 1,254 |
Selectivity Estimation for Range Predicates using Lightweight Models |
2019 |
VLDB |
0.00013027411 |
| 1,300 |
The Picasso Database Query Optimizer Visualizer |
2010 |
VLDB |
0.00012733214 |
| 1,341 |
Dynamic Programming Strikes Back |
2008 |
SIGMOD |
0.00012486285 |
| 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 |
| 1,737 |
QuickSel: Quick Selectivity Learning with Mixture Models |
2020 |
SIGMOD |
0.00010720294 |
| 2,121 |
Balsa: Learning a Query Optimizer Without Expert Demonstrations |
2022 |
SIGMOD |
9.5017232e-05 |
| 2,142 |
Pessimistic Cardinality Estimation: Tighter Upper Bounds for Intermediate Join Cardinalities |
2019 |
SIGMOD |
9.4507296e-05 |
| 2,364 |
Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries |
2020 |
SIGMOD |
8.9554751e-05 |
| 2,762 |
FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation |
2021 |
VLDB |
8.1585394e-05 |
| 2,783 |
Flow-Loss: Learning Cardinality Estimates That Matter |
2021 |
VLDB |
8.1293383e-05 |
| 3,266 |
Learned Cardinality Estimation: An In-depth Study |
2022 |
SIGMOD |
7.3074684e-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 |
| 3,499 |
Fauce: Fast and Accurate Deep Ensembles with Uncertainty for Cardinality Estimation |
2021 |
VLDB |
7.0376445e-05 |
| 3,727 |
Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection |
2022 |
VLDB |
6.8141709e-05 |
| 3,828 |
Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction |
2022 |
VLDB |
6.7208524e-05 |
| 3,924 |
A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation |
2021 |
SIGMOD |
6.6271553e-05 |
| 3,990 |
FactorJoin: A New Cardinality Estimation Framework for Join Queries |
2023 |
SIGMOD |
6.5581983e-05 |
| 4,359 |
Astrid: Accurate Selectivity Estimation for String Predicates using Deep Learning |
2021 |
VLDB |
6.2569955e-05 |
| 4,417 |
Robust Query Driven Cardinality Estimation under Changing Workloads |
2023 |
VLDB |
6.2037371e-05 |
| 4,462 |
LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans |
2023 |
VLDB |
6.1611784e-05 |
| 4,523 |
Simplicity Done Right for Join Ordering |
2021 |
CIDR |
6.1135504e-05 |
| 5,334 |
LEON: A New Framework for ML-Aided Query Optimization |
2023 |
VLDB |
5.5649836e-05 |
| 5,423 |
Kepler: Robust Learning for Faster Parametric Query Optimization |
2023 |
SIGMOD |
5.5130233e-05 |
| 5,633 |
Analyzing the Impact of Cardinality Estimation on Execution Plans in Microsoft SQL Server |
2023 |
VLDB |
5.4011156e-05 |
| 5,640 |
AutoSteer: Learned Query Optimization for Any SQL Database |
2023 |
VLDB |
5.3933314e-05 |
| 5,880 |
COMPASS: Online Sketch-based Query Optimization for In-Memory Databases |
2021 |
SIGMOD |
5.2898074e-05 |
| 5,930 |
FASTgres: Making Learned Query Optimizer Hinting Effective |
2023 |
VLDB |
5.2682075e-05 |
| 5,972 |
SafeBound: A Practical System for Generating Cardinality Bounds |
2023 |
SIGMOD |
5.2474768e-05 |
| 6,667 |
Leveraging Query Logs and Machine Learning for Parametric Query Optimization |
2022 |
VLDB |
4.9688874e-05 |
| 7,011 |
Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis |
2023 |
VLDB |
4.8629458e-05 |
| 7,221 |
Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation |
2023 |
SIGMOD |
4.797194e-05 |
| 7,330 |
Lemo: A Cache-Enhanced Learned Optimizer for Concurrent Queries |
2023 |
SIGMOD |
4.7609373e-05 |
| 7,753 |
Rethinking Learned Cost Models: Why Start from Scratch? |
2023 |
SIGMOD |
4.660151e-05 |
| 8,164 |
Efficiently Computing Join Orders with Heuristic Search |
2023 |
SIGMOD |
4.5718104e-05 |
| 8,624 |
A Study of Database Performance Sensitivity to Experiment Settings |
2022 |
VLDB |
4.483049e-05 |
| 9,108 |
BASE: Bridging the Gap between Cost and Latency for Query Optimization |
2023 |
VLDB |
4.3950066e-05 |
| 9,628 |
Approximate Sketches |
2024 |
SIGMOD |
4.3143499e-05 |