| 318 |
DeepDB: Learn from Data, not from Queries! |
2020 |
VLDB |
0.00021167555 |
| 362 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00019989474 |
| 437 |
QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning |
2019 |
VLDB |
0.00018315867 |
| 512 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00017050173 |
| 982 |
Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation |
2022 |
VLDB |
0.00012714044 |
| 1,064 |
Are We Ready For Learned Cardinality Estimation? |
2021 |
VLDB |
0.00012202282 |
| 1,199 |
Balsa: Learning a Query Optimizer Without Expert Demonstrations |
2022 |
SIGMOD |
0.00011563985 |
| 1,580 |
Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries |
2020 |
SIGMOD |
0.00010180835 |
| 1,734 |
Flow-Loss: Learning Cardinality Estimates That Matter |
2021 |
VLDB |
9.7545773e-05 |
| 2,210 |
Lero: A Learning-to-Rank Query Optimizer |
2023 |
VLDB |
8.8257742e-05 |
| 2,250 |
QueryFormer: A Tree Transformer Model for Query Plan Representation |
2022 |
VLDB |
8.7533306e-05 |
| 2,342 |
Learned Cardinality Estimation: An In-depth Study |
2022 |
SIGMOD |
8.6060437e-05 |
| 2,395 |
A Learned Query Rewrite System using Monte Carlo Tree Search |
2022 |
VLDB |
8.5281914e-05 |
| 2,478 |
Learning a Partitioning Advisor for Cloud Databases |
2020 |
SIGMOD |
8.4079121e-05 |
| 2,522 |
Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation |
2022 |
VLDB |
8.3477168e-05 |
| 2,634 |
Neural Subgraph Counting with Wasserstein Estimator |
2022 |
SIGMOD |
8.1993804e-05 |
| 2,690 |
Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection |
2022 |
VLDB |
8.1258173e-05 |
| 2,842 |
Query Performance Prediction for Concurrent Queries using Graph Embedding |
2020 |
VLDB |
7.949193e-05 |
| 2,846 |
FactorJoin: A New Cardinality Estimation Framework for Join Queries |
2023 |
SIGMOD |
7.9453616e-05 |
| 2,885 |
Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction |
2022 |
VLDB |
7.9094988e-05 |
| 2,908 |
AI Meets Database: AI4DB and DB4AI |
2021 |
SIGMOD |
7.8742664e-05 |
| 3,052 |
A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation |
2021 |
SIGMOD |
7.7052471e-05 |
| 3,160 |
A Learned Sketch for Subgraph Counting |
2021 |
SIGMOD |
7.5807496e-05 |
| 3,487 |
LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans |
2023 |
VLDB |
7.263041e-05 |
| 3,562 |
Astrid: Accurate Selectivity Estimation for String Predicates using Deep Learning |
2021 |
VLDB |
7.2046519e-05 |
| 3,682 |
openGauss: An Autonomous Database System |
2021 |
VLDB |
7.1013922e-05 |
| 3,703 |
Stable Learned Bloom Filters for Data Streams |
2020 |
VLDB |
7.0842566e-05 |
| 3,741 |
FACE: A Normalizing Flow based Cardinality Estimator |
2022 |
VLDB |
7.0594076e-05 |
| 3,965 |
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems |
2021 |
SIGMOD |
6.8918628e-05 |
| 3,978 |
Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload |
2021 |
SIGMOD |
6.8807882e-05 |
| 4,079 |
Towards Dynamic and Safe Configuration Tuning for Cloud Databases |
2022 |
SIGMOD |
6.818264e-05 |
| 4,137 |
The Art of Balance: A RateupDB Experience of Building a CPU/GPU Hybrid Database Product |
2021 |
VLDB |
6.7861661e-05 |
| 4,258 |
LEON: A New Framework for ML-Aided Query Optimization |
2023 |
VLDB |
6.6994722e-05 |
| 4,457 |
Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process |
2022 |
SIGMOD |
6.5913732e-05 |
| 4,538 |
One Model to Rule them All: Towards Zero-Shot Learning for Databases |
2022 |
CIDR |
6.553705e-05 |
| 4,688 |
Learned Cardinality Estimation for Similarity Queries |
2021 |
SIGMOD |
6.4697463e-05 |
| 4,707 |
PreQR: Pre-training Representation for SQL Understanding |
2022 |
SIGMOD |
6.4587914e-05 |
| 4,711 |
Learned Index Benefits: Machine Learning Based Index Performance Estimation |
2022 |
VLDB |
6.4573842e-05 |
| 4,741 |
Machine Learning for Databases |
2021 |
VLDB |
6.4410027e-05 |
| 4,781 |
Learned Approximate Query Processing: Make it Light, Accurate and Fast |
2021 |
CIDR |
6.4162085e-05 |
| 5,058 |
Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing |
2022 |
VLDB |
6.2926774e-05 |
| 5,126 |
Monotonic Cardinality Estimation of Similarity Selection: A Deep Learning Approach |
2020 |
SIGMOD |
6.261175e-05 |
| 5,194 |
Database Workload Characterization with Query Plan Encoders |
2022 |
VLDB |
6.2353557e-05 |
| 5,214 |
Stage: Query Execution Time Prediction in Amazon Redshift |
2024 |
SIGMOD |
6.2248104e-05 |
| 5,241 |
FASTgres: Making Learned Query Optimizer Hinting Effective |
2023 |
VLDB |
6.2154384e-05 |
| 5,456 |
Eraser: Eliminating Performance Regression on Learned Query Optimizer |
2024 |
VLDB |
6.1239873e-05 |
| 5,481 |
A Comparative Study and Component Analysis of Query Plan Representation Techniques in ML4DB Studies |
2024 |
VLDB |
6.1125124e-05 |
| 5,649 |
Sample-Efficient Cardinality Estimation Using Geometric Deep Learning |
2024 |
VLDB |
6.052326e-05 |
| 5,683 |
How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks |
2025 |
SIGMOD |
6.0392183e-05 |
| 5,865 |
Modeling Shifting Workloads for Learned Database Systems |
2024 |
SIGMOD |
5.9659203e-05 |