| 394 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00019400224 |
| 462 |
An End-to-End Learning-based Cost Estimator |
2020 |
VLDB |
0.0001813892 |
| 1,481 |
Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms |
2020 |
VLDB |
0.00010699268 |
| 1,742 |
Updatable Learned Index with Precise Positions |
2021 |
VLDB |
9.9382185e-05 |
| 2,134 |
DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems |
2021 |
VLDB |
9.1799543e-05 |
| 2,329 |
QueryFormer: A Tree Transformer Model for Query Plan Representation |
2022 |
VLDB |
8.8228164e-05 |
| 2,487 |
Active Learning for ML Enhanced Database Systems |
2020 |
SIGMOD |
8.5859375e-05 |
| 2,506 |
Lero: A Learning-to-Rank Query Optimizer |
2023 |
VLDB |
8.5603022e-05 |
| 2,682 |
A Learned Query Rewrite System using Monte Carlo Tree Search |
2022 |
VLDB |
8.3248096e-05 |
| 2,781 |
Query Performance Prediction for Concurrent Queries using Graph Embedding |
2020 |
VLDB |
8.1921284e-05 |
| 2,842 |
Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection |
2022 |
VLDB |
8.1092924e-05 |
| 2,850 |
AI Meets Database: AI4DB and DB4AI |
2021 |
SIGMOD |
8.1022388e-05 |
| 2,929 |
Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction |
2022 |
VLDB |
8.0220847e-05 |
| 3,108 |
FactorJoin: A New Cardinality Estimation Framework for Join Queries |
2023 |
SIGMOD |
7.8122622e-05 |
| 3,164 |
Estimating Cardinalities with Deep Sketches |
2019 |
SIGMOD |
7.7464612e-05 |
| 3,555 |
Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation |
2021 |
VLDB |
7.3766884e-05 |
| 3,632 |
openGauss: An Autonomous Database System |
2021 |
VLDB |
7.3121856e-05 |
| 3,755 |
Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift |
2023 |
SIGMOD |
7.2037909e-05 |
| 3,911 |
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems |
2021 |
SIGMOD |
7.0870659e-05 |
| 4,291 |
Stable Learned Bloom Filters for Data Streams |
2020 |
VLDB |
6.849278e-05 |
| 4,417 |
One Model to Rule them All: Towards Zero-Shot Learning for Databases |
2022 |
CIDR |
6.7766173e-05 |
| 4,507 |
LEON: A New Framework for ML-Aided Query Optimization |
2023 |
VLDB |
6.723287e-05 |
| 4,585 |
Learned Index Benefits: Machine Learning Based Index Performance Estimation |
2022 |
VLDB |
6.6883557e-05 |
| 4,944 |
Monotonic Cardinality Estimation of Similarity Selection: A Deep Learning Approach |
2020 |
SIGMOD |
6.4987159e-05 |
| 5,009 |
Database Workload Characterization with Query Plan Encoders |
2022 |
VLDB |
6.4710516e-05 |
| 5,206 |
Stage: Query Execution Time Prediction in Amazon Redshift |
2024 |
SIGMOD |
6.3868465e-05 |
| 5,283 |
Machine Learning for Databases |
2021 |
VLDB |
6.3568612e-05 |
| 5,366 |
Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing |
2022 |
VLDB |
6.3169865e-05 |
| 5,489 |
HMAB: Self-Driving Hierarchy of Bandits for Integrated Physical Database Design Tuning |
2023 |
VLDB |
6.2678203e-05 |
| 5,503 |
Eraser: Eliminating Performance Regression on Learned Query Optimizer |
2024 |
VLDB |
6.2627904e-05 |
| 5,696 |
A Comparative Study and Component Analysis of Query Plan Representation Techniques in ML4DB Studies |
2024 |
VLDB |
6.1834438e-05 |
| 5,965 |
Expand your Training Limits! Generating Training Data for ML-based Data Management |
2021 |
SIGMOD |
6.0876627e-05 |
| 6,282 |
How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks |
2025 |
SIGMOD |
5.9906069e-05 |
| 6,284 |
A Unified Transferable Model for ML-Enhanced DBMS |
2022 |
CIDR |
5.9902184e-05 |
| 6,970 |
Refactoring Index Tuning Process with Benefit Estimation |
2024 |
VLDB |
5.7967533e-05 |
| 7,074 |
Modeling Shifting Workloads for Learned Database Systems |
2024 |
SIGMOD |
5.7634614e-05 |
| 7,269 |
Rethinking Learned Cost Models: Why Start from Scratch? |
2023 |
SIGMOD |
5.7128028e-05 |
| 7,448 |
Cost-Intelligent Data Analytics in the Cloud |
2024 |
CIDR |
5.677292e-05 |
| 7,632 |
Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRAD |
2024 |
VLDB |
5.6369542e-05 |
| 7,644 |
CAMAL: Optimizing LSM-trees via Active Learning |
2024 |
SIGMOD |
5.6327117e-05 |
| 7,664 |
Tastes Great! Less Filling! High Performance and Accurate Training Data Collection for Self-Driving Database Management Systems |
2022 |
SIGMOD |
5.6285439e-05 |
| 7,722 |
The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions |
2024 |
VLDB |
5.6186987e-05 |
| 8,295 |
Robust and Budget-Constrained Encoding Configurations for In-Memory Database Systems |
2022 |
VLDB |
5.5140539e-05 |
| 8,463 |
Learned Offline Query Planning via Bayesian Optimization |
2025 |
SIGMOD |
5.4900503e-05 |
| 8,666 |
Tiresias: Enabling Predictive Autonomous Storage and Indexing |
2022 |
VLDB |
5.4572677e-05 |
| 8,842 |
T3: Accurate and Fast Performance Prediction for Relational Database Systems With Compiled Decision Trees |
2025 |
SIGMOD |
5.4241294e-05 |
| 8,858 |
Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems |
2024 |
VLDB |
5.4212377e-05 |
| 9,091 |
Phoebe: A Learning-based Checkpoint Optimizer |
2021 |
VLDB |
5.3855277e-05 |
| 9,290 |
LIMAO: A Framework for Lifelong Modular Learned Query Optimization |
2025 |
VLDB |
5.3525489e-05 |
| 9,327 |
Are Joins over LSM-trees Ready? Take RocksDB as an Example |
2025 |
VLDB |
5.3449422e-05 |