| 606 |
DeepDB: Learn from Data, not from Queries! |
2020 |
VLDB |
0.00019251186 |
| 752 |
Deep Unsupervised Cardinality Estimation |
2020 |
VLDB |
0.00017138049 |
| 905 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00015423174 |
| 1,638 |
Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation |
2022 |
VLDB |
0.00011050093 |
| 2,090 |
Balsa: Learning a Query Optimizer Without Expert Demonstrations |
2022 |
SIGMOD |
9.5668285e-05 |
| 2,550 |
Updatable Learned Index with Precise Positions |
2021 |
VLDB |
8.5569576e-05 |
| 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 |
| 3,241 |
A Learned Query Rewrite System using Monte Carlo Tree Search |
2022 |
VLDB |
7.32744e-05 |
| 3,466 |
AI Meets Database: AI4DB and DB4AI |
2021 |
SIGMOD |
7.0645718e-05 |
| 3,623 |
Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings |
2020 |
SIGMOD |
6.9017341e-05 |
| 3,725 |
Estimating Cardinalities with Deep Sketches |
2019 |
SIGMOD |
6.8117015e-05 |
| 3,819 |
Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction |
2022 |
VLDB |
6.7267885e-05 |
| 3,866 |
Cloudy with High Chance of DBMS: A 10-year Prediction for Enterprise-Grade ML |
2020 |
CIDR |
6.6795497e-05 |
| 3,924 |
A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation |
2021 |
SIGMOD |
6.6227223e-05 |
| 3,955 |
Efficiently Approximating Selectivity Functions using Low Overhead Regression Models |
2020 |
VLDB |
6.5895015e-05 |
| 3,992 |
FactorJoin: A New Cardinality Estimation Framework for Join Queries |
2023 |
SIGMOD |
6.5519369e-05 |
| 4,086 |
The Case for a Learned Sorting Algorithm |
2020 |
SIGMOD |
6.4579358e-05 |
| 4,377 |
HUNTER: An Online Cloud Database Hybrid Tuning System for Personalized Requirements |
2022 |
SIGMOD |
6.2331947e-05 |
| 4,448 |
Stable Learned Bloom Filters for Data Streams |
2020 |
VLDB |
6.1741316e-05 |
| 4,587 |
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems |
2021 |
SIGMOD |
6.0594195e-05 |
| 4,687 |
Deploying a Steered Query Optimizer in Production at Microsoft |
2022 |
SIGMOD |
5.9915268e-05 |
| 5,373 |
Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing |
2022 |
VLDB |
5.5410059e-05 |
| 5,405 |
ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads |
2024 |
VLDB |
5.5243727e-05 |
| 5,501 |
To Share, or not to Share Online Event Trend Aggregation Over Bursty Event Streams |
2021 |
SIGMOD |
5.4731481e-05 |
| 5,799 |
Learned Approximate Query Processing: Make it Light, Accurate and Fast |
2021 |
CIDR |
5.3219666e-05 |
| 5,944 |
SAM: Database Generation from Query Workloads with Supervised Autoregressive Models |
2022 |
SIGMOD |
5.2583712e-05 |
| 5,994 |
Steering Query Optimizers: A Practical Take on Big Data Workloads |
2021 |
SIGMOD |
5.2367998e-05 |
| 6,278 |
The Cosmos Big Data Platform at Microsoft: Over a Decade of Progress and a Decade to Look Forward |
2021 |
VLDB |
5.1241654e-05 |
| 7,465 |
Yannakakis+: Practical Acyclic Query Evaluation with Theoretical Guarantees |
2025 |
SIGMOD |
4.7186055e-05 |
| 7,564 |
Modeling Shifting Workloads for Learned Database Systems |
2024 |
SIGMOD |
4.7049893e-05 |
| 7,652 |
Machine Learning for Cloud Data Systems: the Progress so far and the Path Forward |
2021 |
VLDB |
4.6831938e-05 |
| 7,685 |
AutoToken: Predicting Peak Parallelism for Big Data Analytics at Microsoft |
2020 |
VLDB |
4.6753414e-05 |
| 7,809 |
Sibyl: Forecasting Time-Evolving Query Workloads |
2024 |
SIGMOD |
4.6415559e-05 |
| 8,196 |
SparkCruise: Workload Optimization in Managed Spark Clusters at Microsoft |
2021 |
VLDB |
4.5568952e-05 |
| 8,219 |
PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost! |
2021 |
VLDB |
4.551524e-05 |
| 8,378 |
Towards Building Autonomous Data Services on Azure |
2023 |
SIGMOD |
4.5275731e-05 |
| 8,579 |
Towards Query Optimizer as a Service (QOaaS) in a Unified LakeHouse Ecosystem: Can One QO Rule Them All? |
2025 |
CIDR |
4.4877266e-05 |
| 9,138 |
Phoebe: A Learning-based Checkpoint Optimizer |
2021 |
VLDB |
4.3842765e-05 |
| 9,215 |
PACE: Poisoning Attacks on Learned Cardinality Estimation |
2024 |
SIGMOD |
4.3679174e-05 |
| 9,811 |
A Practical Theory of Generalization in Selectivity Learning |
2025 |
VLDB |
4.2742278e-05 |
| 9,877 |
PRICE: A Pretrained Model for Cross-Database Cardinality Estimation |
2025 |
VLDB |
4.2615675e-05 |
| 10,863 |
Graph Transformers for Query Plan Representation: Potentials and Challenges |
2025 |
VLDB |
4.1905499e-05 |
| 10,998 |
Understanding and Reusing Test Suites Across Database Systems |
2024 |
SIGMOD |
4.1905499e-05 |