| 362 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00019989474 |
| 461 |
An End-to-End Learning-based Cost Estimator |
2020 |
VLDB |
0.00017829982 |
| 1,397 |
Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms |
2020 |
VLDB |
0.00010789242 |
| 1,515 |
DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems |
2021 |
VLDB |
0.00010417728 |
| 1,550 |
Updatable Learned Index with Precise Positions |
2021 |
VLDB |
0.00010282449 |
| 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 |
| 2,275 |
Active Learning for ML Enhanced Database Systems |
2020 |
SIGMOD |
8.7090584e-05 |
| 2,395 |
A Learned Query Rewrite System using Monte Carlo Tree Search |
2022 |
VLDB |
8.5281914e-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 |
| 3,271 |
Estimating Cardinalities with Deep Sketches |
2019 |
SIGMOD |
7.4744941e-05 |
| 3,563 |
Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift |
2023 |
SIGMOD |
7.2042148e-05 |
| 3,590 |
Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation |
2021 |
VLDB |
7.1865343e-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 |
| 3,965 |
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems |
2021 |
SIGMOD |
6.8918628e-05 |
| 4,258 |
LEON: A New Framework for ML-Aided Query Optimization |
2023 |
VLDB |
6.6994722e-05 |
| 4,538 |
One Model to Rule them All: Towards Zero-Shot Learning for Databases |
2022 |
CIDR |
6.553705e-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 |
| 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 |
| 5,456 |
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 |
| 5,674 |
HMAB: Self-Driving Hierarchy of Bandits for Integrated Physical Database Design Tuning |
2023 |
VLDB |
6.0430252e-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 |
| 6,141 |
Expand your Training Limits! Generating Training Data for ML-based Data Management |
2021 |
SIGMOD |
5.8733296e-05 |
| 6,416 |
A Unified Transferable Model for ML-Enhanced DBMS |
2022 |
CIDR |
5.7920805e-05 |
| 7,033 |
Rethinking Learned Cost Models: Why Start from Scratch? |
2023 |
SIGMOD |
5.6168499e-05 |
| 7,160 |
Cost-Intelligent Data Analytics in the Cloud |
2024 |
CIDR |
5.5962258e-05 |
| 7,217 |
Refactoring Index Tuning Process with Benefit Estimation |
2024 |
VLDB |
5.5834823e-05 |
| 7,460 |
T3: Accurate and Fast Performance Prediction for Relational Database Systems With Compiled Decision Trees |
2025 |
SIGMOD |
5.5215755e-05 |
| 7,864 |
Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRAD |
2024 |
VLDB |
5.4367919e-05 |
| 7,905 |
CAMAL: Optimizing LSM-trees via Active Learning |
2024 |
SIGMOD |
5.4291824e-05 |
| 7,915 |
Tastes Great! Less Filling! High Performance and Accurate Training Data Collection for Self-Driving Database Management Systems |
2022 |
SIGMOD |
5.4276987e-05 |
| 7,977 |
The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions |
2024 |
VLDB |
5.4142519e-05 |
| 8,332 |
Learned Offline Query Planning via Bayesian Optimization |
2025 |
SIGMOD |
5.3528188e-05 |
| 8,563 |
Robust and Budget-Constrained Encoding Configurations for In-Memory Database Systems |
2022 |
VLDB |
5.3138647e-05 |
| 8,872 |
Tiresias: Enabling Predictive Autonomous Storage and Indexing |
2022 |
VLDB |
5.2584643e-05 |
| 9,115 |
Check Out the Big Brain on BRAD: Simplifying Cloud Data Processing with Learned Automated Data Meshes |
2023 |
VLDB |
5.2271833e-05 |
| 9,133 |
Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems |
2024 |
VLDB |
5.2229655e-05 |
| 9,358 |
Phoebe: A Learning-based Checkpoint Optimizer |
2021 |
VLDB |
5.1869771e-05 |
| 9,546 |
Athena: An Effective Learning-based Framework for Query Optimizer Performance Improvement |
2025 |
SIGMOD |
5.1604755e-05 |