Back to papers
A Unified Transferable Model for ML-Enhanced DBMS
Summary: MTMLF: unified transferable ML for DBMS, using multi-task training to capture cross-task signals and pretrain–fine-tune to distill meta-knowledge across databases, eliminating expensive per-DB retraining and large new-data needs. Demonstrated for query optimization.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 462
- Venue
- CIDR
- Year
- 2022
- Pagerank
- 4.9253635e-05
- Overall Rank
- 6,774 | 52.93%
- DOI
-
-
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 13 of 13 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 1,638 |
Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation |
2022 |
VLDB |
0.00011050093 |
| 3,992 |
FactorJoin: A New Cardinality Estimation Framework for Join Queries |
2023 |
SIGMOD |
6.5519369e-05 |
| 5,405 |
ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads |
2024 |
VLDB |
5.5243727e-05 |
| 6,328 |
A Comparative Study and Component Analysis of Query Plan Representation Techniques in ML4DB Studies |
2024 |
VLDB |
5.1034426e-05 |
| 6,382 |
Sample-Efficient Cardinality Estimation Using Geometric Deep Learning |
2024 |
VLDB |
5.0835686e-05 |
| 6,687 |
How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks |
2025 |
SIGMOD |
4.957987e-05 |
| 6,860 |
Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data |
2023 |
SIGMOD |
4.9008421e-05 |
| 7,994 |
Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRAD |
2024 |
VLDB |
4.607322e-05 |
| 8,834 |
ByteCard: Enhancing ByteDance’s Data Warehouse with Learned Cardinality Estimation |
2024 |
SIGMOD |
4.4351469e-05 |
| 8,847 |
Towards Foundation Database Models |
2025 |
CIDR |
4.4329366e-05 |
| 9,012 |
Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems |
2024 |
VLDB |
4.4059413e-05 |
| 9,877 |
PRICE: A Pretrained Model for Cross-Database Cardinality Estimation |
2025 |
VLDB |
4.2615675e-05 |
| 10,300 |
TATA: An Efficient Framework for Task Transfer in Query Plan Representation |
2026 |
VLDB |
4.1905499e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 22 of 22 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 71 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00059446482 |
| 101 |
The Case for Learned Index Structures |
2018 |
SIGMOD |
0.00049778866 |
| 203 |
Learned Cardinalities: Estimating Correlated Joins with Deep Learning |
2019 |
CIDR |
0.00034868567 |
| 329 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00027301488 |
| 510 |
An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning |
2019 |
SIGMOD |
0.00021420477 |
| 514 |
TURL: Table Understanding through Representation Learning |
2021 |
VLDB |
0.00021280726 |
| 606 |
DeepDB: Learn from Data, not from Queries! |
2020 |
VLDB |
0.00019251186 |
| 752 |
Deep Unsupervised Cardinality Estimation |
2020 |
VLDB |
0.00017138049 |
| 779 |
QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning |
2019 |
VLDB |
0.00016719473 |
| 804 |
An End-to-End Learning-based Cost Estimator |
2020 |
VLDB |
0.0001643674 |
| 819 |
ALEX: An Updatable Adaptive Learned Index |
2020 |
SIGMOD |
0.00016237497 |
| 876 |
Plan-Structured Deep Neural Network Models for Query Performance Prediction |
2019 |
VLDB |
0.00015660534 |
| 905 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00015423174 |
| 1,464 |
Learning Multi-dimensional Indexes |
2020 |
SIGMOD |
0.0001184772 |
| 1,638 |
Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation |
2022 |
VLDB |
0.00011050093 |
| 1,887 |
Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads |
2021 |
VLDB |
0.00010201938 |
| 2,769 |
FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation |
2021 |
VLDB |
8.1512848e-05 |
| 2,781 |
Flow-Loss: Learning Cardinality Estimates That Matter |
2021 |
VLDB |
8.1282042e-05 |
| 3,144 |
Active Learning for ML Enhanced Database Systems |
2020 |
SIGMOD |
7.4844943e-05 |
| 3,222 |
WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases |
2016 |
VLDB |
7.3531422e-05 |
| 3,623 |
Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings |
2020 |
SIGMOD |
6.9017341e-05 |
| 5,696 |
Exact Cardinality Query Optimization with Bounded Execution Cost |
2019 |
SIGMOD |
5.367449e-05 |
Semantically Similar Papers
| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 11,284 |
Demonstration of OpenDBML, a Framework for Democratizing In-Database Machine Learning |
2023 |
VLDB |
4.1905499e-05 |
| 510 |
An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning |
2019 |
SIGMOD |
0.00021420477 |
| 3,122 |
DB4ML – An In-Memory Database Kernel with Machine Learning Support |
2020 |
SIGMOD |
7.5284233e-05 |
| 9,469 |
Database Gyms |
2023 |
CIDR |
4.3304872e-05 |
| 6,376 |
A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning |
2023 |
SIGMOD |
5.0861082e-05 |
| 9,775 |
Structure-Aware Machine Learning over Multi-Relational Databases |
2021 |
SIGMOD |
4.2815042e-05 |
| 5,250 |
One Model to Rule them All: Towards Zero-Shot Learning for Databases |
2022 |
CIDR |
5.6007779e-05 |
| 8,847 |
Towards Foundation Database Models |
2025 |
CIDR |
4.4329366e-05 |
| 5,787 |
Machine Learning for Databases |
2021 |
VLDB |
5.3256401e-05 |
| 3,866 |
Cloudy with High Chance of DBMS: A 10-year Prediction for Enterprise-Grade ML |
2020 |
CIDR |
6.6795497e-05 |