| 15 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00061463175 |
| 74 |
Automatic Database Management System Tuning Through Large-scale Machine Learning |
2017 |
SIGMOD |
0.00037164008 |
| 224 |
Self-Driving Database Management Systems |
2017 |
CIDR |
0.00023973185 |
| 309 |
An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning |
2019 |
SIGMOD |
0.0002143249 |
| 355 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00020146245 |
| 380 |
AutoAdmin "What-if" Index Analysis Utility |
1998 |
SIGMOD |
0.00019530546 |
| 411 |
QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning |
2019 |
VLDB |
0.0001874043 |
| 458 |
Query-based Workload Forecasting for Self-Driving Database Management Systems |
2018 |
SIGMOD |
0.00017870238 |
| 496 |
Adaptive Self-Tuning Memory in DB2 |
2006 |
VLDB |
0.00017347275 |
| 545 |
Plan-Structured Deep Neural Network Models for Query Performance Prediction |
2019 |
VLDB |
0.0001657599 |
| 756 |
Automatic Physical Database Tuning: A Relaxation-based Approach |
2005 |
SIGMOD |
0.00014209974 |
| 1,282 |
AI Meets AI: Leveraging Query Executions to Improve Index Recommendations |
2019 |
SIGMOD |
0.00011197535 |
| 1,400 |
Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms |
2020 |
VLDB |
0.00010759064 |
| 1,513 |
DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems |
2021 |
VLDB |
0.00010394557 |
| 1,600 |
ReAcTable: Enhancing ReAct for Table Question Answering |
2024 |
VLDB |
0.00010093108 |
| 1,656 |
D-Bot: Database Diagnosis System using Large Language Models |
2024 |
VLDB |
9.948529e-05 |
| 1,791 |
Why TPC Is Not Enough: An Analysis of the Amazon Redshift Fleet |
2024 |
VLDB |
9.6212919e-05 |
| 1,956 |
LLM-R^2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency |
2025 |
VLDB |
9.2927535e-05 |
| 2,169 |
QueryFormer: A Tree Transformer Model for Query Plan Representation |
2022 |
VLDB |
8.9151935e-05 |
| 2,268 |
ELPIS: Graph-Based Similarity Search for Scalable Data Science |
2023 |
VLDB |
8.7144632e-05 |
| 2,290 |
Active Learning for ML Enhanced Database Systems |
2020 |
SIGMOD |
8.6813363e-05 |
| 2,619 |
Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction |
2022 |
VLDB |
8.1985665e-05 |
| 2,772 |
Instance-Optimized Data Layouts for Cloud Analytics Workloads |
2021 |
SIGMOD |
8.0148928e-05 |
| 2,779 |
Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation |
2022 |
VLDB |
8.0085666e-05 |
| 3,057 |
LlamaTune: Sample-Efficient DBMS Configuration Tuning |
2022 |
VLDB |
7.6865211e-05 |
| 3,269 |
Robust Query Driven Cardinality Estimation under Changing Workloads |
2023 |
VLDB |
7.463682e-05 |
| 3,358 |
Combining Small Language Models and Large Language Models for Zero-Shot NL2SQL |
2024 |
VLDB |
7.3650961e-05 |
| 3,481 |
LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans |
2023 |
VLDB |
7.2463722e-05 |
| 3,575 |
Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation |
2021 |
VLDB |
7.1769215e-05 |
| 3,655 |
UDO: Universal Database Optimization using Reinforcement Learning |
2021 |
VLDB |
7.1137504e-05 |
| 3,839 |
Automatic Database Configuration Debugging using Retrieval-Augmented Language Models |
2025 |
SIGMOD |
6.9704205e-05 |
| 3,959 |
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems |
2021 |
SIGMOD |
6.8804723e-05 |
| 4,022 |
Automated Generation of Materialized Views in Oracle |
2020 |
VLDB |
6.8345671e-05 |
| 4,247 |
LEON: A New Framework for ML-Aided Query Optimization |
2023 |
VLDB |
6.6920059e-05 |
| 4,459 |
Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts |
2022 |
SIGMOD |
6.5757572e-05 |
| 4,489 |
Real-time Workload Pattern Analysis for Large-scale Cloud Databases |
2023 |
VLDB |
6.5619575e-05 |
| 4,641 |
How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks |
2025 |
SIGMOD |
6.4739208e-05 |
| 4,683 |
AutoSteer: Learned Query Optimization for Any SQL Database |
2023 |
VLDB |
6.4545989e-05 |
| 4,726 |
Learned Index Benefits: Machine Learning Based Index Performance Estimation |
2022 |
VLDB |
6.4329878e-05 |
| 4,879 |
The RLR-Tree: A Reinforcement Learning Based R-Tree for Spatial Data |
2023 |
SIGMOD |
6.3567479e-05 |
| 4,935 |
METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection |
2024 |
VLDB |
6.3343977e-05 |
| 4,990 |
Budget-aware Index Tuning with Reinforcement Learning |
2022 |
SIGMOD |
6.3120056e-05 |
| 5,044 |
Analyzing the Impact of Cardinality Estimation on Execution Plans in Microsoft SQL Server |
2023 |
VLDB |
6.2888728e-05 |
| 5,304 |
An Efficient Transfer Learning Based Configuration Adviser for Database Tuning |
2024 |
VLDB |
6.1758143e-05 |
| 5,950 |
Doppler: Automated SKU Recommendation in Migrating SQL Workloads to the Cloud |
2022 |
VLDB |
5.9187223e-05 |
| 6,100 |
Dear User-Defined Functions, Inlining isn't working out so great for us. Let's try batching to make our relationship work. Sincerely, SQL |
2024 |
CIDR |
5.8699028e-05 |
| 6,109 |
Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective |
2024 |
VLDB |
5.8676956e-05 |
| 6,672 |
Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation |
2023 |
SIGMOD |
5.6975871e-05 |
| 6,752 |
A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning |
2023 |
SIGMOD |
5.6733557e-05 |
| 6,812 |
dbET: Execution Time Distribution-based Plan Selection |
2023 |
SIGMOD |
5.6597129e-05 |