| 608 |
DeepDB: Learn from Data, not from Queries! |
2020 |
VLDB |
0.00019235898 |
| 640 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00018759152 |
| 782 |
QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning |
2019 |
VLDB |
0.00016729063 |
| 910 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00015423056 |
| 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 |
| 2,121 |
Balsa: Learning a Query Optimizer Without Expert Demonstrations |
2022 |
SIGMOD |
9.5017232e-05 |
| 2,364 |
Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries |
2020 |
SIGMOD |
8.9554751e-05 |
| 2,783 |
Flow-Loss: Learning Cardinality Estimates That Matter |
2021 |
VLDB |
8.1293383e-05 |
| 3,001 |
Neural Subgraph Counting with Wasserstein Estimator |
2022 |
SIGMOD |
7.7404487e-05 |
| 3,076 |
Learning a Partitioning Advisor for Cloud Databases |
2020 |
SIGMOD |
7.6107677e-05 |
| 3,169 |
QueryFormer: A Tree Transformer Model for Query Plan Representation |
2022 |
VLDB |
7.4498425e-05 |
| 3,248 |
A Learned Query Rewrite System using Monte Carlo Tree Search |
2022 |
VLDB |
7.3258782e-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 |
| 3,473 |
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 |
| 3,727 |
Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection |
2022 |
VLDB |
6.8141709e-05 |
| 3,778 |
A Learned Sketch for Subgraph Counting |
2021 |
SIGMOD |
6.7747398e-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 |
| 4,152 |
openGauss: An Autonomous Database System |
2021 |
VLDB |
6.4060406e-05 |
| 4,359 |
Astrid: Accurate Selectivity Estimation for String Predicates using Deep Learning |
2021 |
VLDB |
6.2569955e-05 |
| 4,434 |
Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process |
2022 |
SIGMOD |
6.1929999e-05 |
| 4,446 |
Stable Learned Bloom Filters for Data Streams |
2020 |
VLDB |
6.1800659e-05 |
| 4,462 |
LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans |
2023 |
VLDB |
6.1611784e-05 |
| 4,543 |
FACE: A Normalizing Flow based Cardinality Estimator |
2022 |
VLDB |
6.1011198e-05 |
| 4,590 |
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems |
2021 |
SIGMOD |
6.0620053e-05 |
| 4,661 |
PreQR: Pre-training Representation for SQL Understanding |
2022 |
SIGMOD |
6.0137947e-05 |
| 4,804 |
Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload |
2021 |
SIGMOD |
5.910467e-05 |
| 4,842 |
Towards Dynamic and Safe Configuration Tuning for Cloud Databases |
2022 |
SIGMOD |
5.8826802e-05 |
| 5,125 |
The Art of Balance: A RateupDBTM Experience of Building a CPU/GPU Hybrid Database Product |
2021 |
VLDB |
5.679423e-05 |
| 5,258 |
One Model to Rule them All: Towards Zero-Shot Learning for Databases |
2022 |
CIDR |
5.5998705e-05 |
| 5,334 |
LEON: A New Framework for ML-Aided Query Optimization |
2023 |
VLDB |
5.5649836e-05 |
| 5,337 |
Learned Index Benefits: Machine Learning Based Index Performance Estimation |
2022 |
VLDB |
5.5635208e-05 |
| 5,368 |
Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing |
2022 |
VLDB |
5.5457532e-05 |
| 5,469 |
Learned Cardinality Estimation for Similarity Queries |
2021 |
SIGMOD |
5.4898192e-05 |
| 5,622 |
Monotonic Cardinality Estimation of Similarity Selection: A Deep Learning Approach |
2020 |
SIGMOD |
5.4060403e-05 |
| 5,637 |
Database Workload Characterization with Query Plan Encoders |
2022 |
VLDB |
5.3979505e-05 |
| 5,832 |
Stage: Query Execution Time Prediction in Amazon Redshift |
2024 |
SIGMOD |
5.3111109e-05 |
| 5,861 |
Machine Learning for Databases |
2021 |
VLDB |
5.298883e-05 |
| 5,930 |
FASTgres: Making Learned Query Optimizer Hinting Effective |
2023 |
VLDB |
5.2682075e-05 |
| 5,952 |
Eraser: Eliminating Performance Regression on Learned Query Optimizer |
2024 |
VLDB |
5.2591691e-05 |
| 6,230 |
Learned Approximate Query Processing: Make it Light, Accurate and Fast |
2021 |
CIDR |
5.145989e-05 |
| 6,297 |
Towards instance-optimized data systems |
2021 |
VLDB |
5.1227886e-05 |
| 6,328 |
A Comparative Study and Component Analysis of Query Plan Representation Techniques in ML4DB Studies |
2024 |
VLDB |
5.1082882e-05 |
| 6,383 |
Sample-Efficient Cardinality Estimation Using Geometric Deep Learning |
2024 |
VLDB |
5.0884322e-05 |
| 6,519 |
Expand your Training Limits! Generating Training Data for ML-based Data Management |
2021 |
SIGMOD |
5.0316686e-05 |