| 640 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00018759152 |
| 806 |
An End-to-End Learning-based Cost Estimator |
2020 |
VLDB |
0.00016434274 |
| 2,020 |
Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms |
2020 |
VLDB |
9.762624e-05 |
| 2,552 |
Updatable Learned Index with Precise Positions |
2021 |
VLDB |
8.5530411e-05 |
| 2,985 |
DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems |
2021 |
VLDB |
7.7795847e-05 |
| 3,142 |
Active Learning for ML Enhanced Database Systems |
2020 |
SIGMOD |
7.4815444e-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 |
| 3,348 |
Lero: A Learning-to-Rank Query Optimizer |
2023 |
VLDB |
7.1904529e-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 |
| 3,725 |
Estimating Cardinalities with Deep Sketches |
2019 |
SIGMOD |
6.8170734e-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 |
| 3,990 |
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 |
| 4,240 |
Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation |
2021 |
VLDB |
6.3318228e-05 |
| 4,446 |
Stable Learned Bloom Filters for Data Streams |
2020 |
VLDB |
6.1800659e-05 |
| 4,590 |
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems |
2021 |
SIGMOD |
6.0620053e-05 |
| 4,593 |
Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift |
2023 |
SIGMOD |
6.0606891e-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 |
| 5,622 |
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 |
| 5,924 |
HMAB: Self-Driving Hierarchy of Bandits for Integrated Physical Database Design Tuning |
2023 |
VLDB |
5.2719183e-05 |
| 5,952 |
Eraser: Eliminating Performance Regression on Learned Query Optimizer |
2024 |
VLDB |
5.2591691e-05 |
| 6,328 |
A Comparative Study and Component Analysis of Query Plan Representation Techniques in ML4DB Studies |
2024 |
VLDB |
5.1082882e-05 |
| 6,519 |
Expand your Training Limits! Generating Training Data for ML-based Data Management |
2021 |
SIGMOD |
5.0316686e-05 |
| 6,685 |
How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks |
2025 |
SIGMOD |
4.9627485e-05 |
| 6,775 |
A Unified Transferable Model for ML-Enhanced DBMS |
2022 |
CIDR |
4.9299192e-05 |
| 7,336 |
Refactoring Index Tuning Process with Benefit Estimation |
2024 |
VLDB |
4.7599411e-05 |
| 7,753 |
Rethinking Learned Cost Models: Why Start from Scratch? |
2023 |
SIGMOD |
4.660151e-05 |
| 7,828 |
Modeling Shifting Workloads for Learned Database Systems |
2024 |
SIGMOD |
4.6407986e-05 |
| 7,889 |
Cost-Intelligent Data Analytics in the Cloud |
2024 |
CIDR |
4.6253386e-05 |
| 7,990 |
Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRAD |
2024 |
VLDB |
4.6117441e-05 |
| 8,009 |
CAMAL: Optimizing LSM-trees via Active Learning |
2024 |
SIGMOD |
4.6066863e-05 |
| 8,020 |
The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions |
2024 |
VLDB |
4.6040862e-05 |
| 8,082 |
Tastes Great! Less Filling! High Performance and Accurate Training Data Collection for Self-Driving Database Management Systems |
2022 |
SIGMOD |
4.5905454e-05 |
| 8,578 |
Robust and Budget-Constrained Encoding Configurations for In-Memory Database Systems |
2022 |
VLDB |
4.4923477e-05 |
| 8,659 |
Learned Offline Query Planning via Bayesian Optimization |
2025 |
SIGMOD |
4.4722928e-05 |
| 8,774 |
Tiresias: Enabling Predictive Autonomous Storage and Indexing |
2022 |
VLDB |
4.4559995e-05 |
| 8,956 |
T3: Accurate and Fast Performance Prediction for Relational Database Systems With Compiled Decision Trees |
2025 |
SIGMOD |
4.4214154e-05 |
| 9,006 |
Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems |
2024 |
VLDB |
4.4101482e-05 |
| 9,194 |
Phoebe: A Learning-based Checkpoint Optimizer |
2021 |
VLDB |
4.3761777e-05 |
| 9,317 |
Are Joins over LSM-trees Ready? Take RocksDB as an Example |
2025 |
VLDB |
4.3556432e-05 |
| 9,345 |
LIMAO: A Framework for Lifelong Modular Learned Query Optimization |
2025 |
VLDB |
4.3536343e-05 |