| 15 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00061067652 |
| 78 |
Automatic Database Management System Tuning Through Large-scale Machine Learning |
2017 |
SIGMOD |
0.00036675568 |
| 224 |
Self-Driving Database Management Systems |
2017 |
CIDR |
0.00024011047 |
| 314 |
An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning |
2019 |
SIGMOD |
0.00021276452 |
| 361 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00020000855 |
| 378 |
AutoAdmin "What-if" Index Analysis Utility |
1998 |
SIGMOD |
0.00019541534 |
| 437 |
QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning |
2019 |
VLDB |
0.00018310278 |
| 461 |
Query-based Workload Forecasting for Self-Driving Database Management Systems |
2018 |
SIGMOD |
0.00017841988 |
| 508 |
Adaptive Self-Tuning Memory in DB2 |
2006 |
VLDB |
0.00017083245 |
| 560 |
Plan-Structured Deep Neural Network Models for Query Performance Prediction |
2019 |
VLDB |
0.00016408613 |
| 751 |
Automatic Physical Database Tuning: A Relaxation-based Approach |
2005 |
SIGMOD |
0.00014251362 |
| 1,280 |
AI Meets AI: Leveraging Query Executions to Improve Index Recommendations |
2019 |
SIGMOD |
0.00011224914 |
| 1,396 |
Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms |
2020 |
VLDB |
0.00010788714 |
| 1,515 |
DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems |
2021 |
VLDB |
0.00010418766 |
| 1,659 |
D-Bot: Database Diagnosis System using Large Language Models |
2024 |
VLDB |
9.9625133e-05 |
| 1,684 |
ReAcTable: Enhancing ReAct for Table Question Answering |
2024 |
VLDB |
9.8776931e-05 |
| 1,834 |
Why TPC Is Not Enough: An Analysis of the Amazon Redshift Fleet |
2024 |
VLDB |
9.5349903e-05 |
| 2,037 |
LLM-R^2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency |
2025 |
VLDB |
9.1494269e-05 |
| 2,248 |
QueryFormer: A Tree Transformer Model for Query Plan Representation |
2022 |
VLDB |
8.7567205e-05 |
| 2,264 |
ELPIS: Graph-Based Similarity Search for Scalable Data Science |
2023 |
VLDB |
8.7286407e-05 |
| 2,278 |
Active Learning for ML Enhanced Database Systems |
2020 |
SIGMOD |
8.7057608e-05 |
| 2,765 |
Instance-Optimized Data Layouts for Cloud Analytics Workloads |
2021 |
SIGMOD |
8.0401855e-05 |
| 2,770 |
Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation |
2022 |
VLDB |
8.0343719e-05 |
| 2,879 |
Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction |
2022 |
VLDB |
7.9126862e-05 |
| 3,052 |
LlamaTune: Sample-Efficient DBMS Configuration Tuning |
2022 |
VLDB |
7.704739e-05 |
| 3,327 |
Robust Query Driven Cardinality Estimation under Changing Workloads |
2023 |
VLDB |
7.4233639e-05 |
| 3,479 |
LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans |
2023 |
VLDB |
7.2665349e-05 |
| 3,489 |
Combining Small Language Models and Large Language Models for Zero-Shot NL2SQL |
2024 |
VLDB |
7.2594757e-05 |
| 3,588 |
Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation |
2021 |
VLDB |
7.1841858e-05 |
| 3,648 |
UDO: Universal Database Optimization using Reinforcement Learning |
2021 |
VLDB |
7.1366536e-05 |
| 3,964 |
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems |
2021 |
SIGMOD |
6.889374e-05 |
| 4,007 |
Automated Generation of Materialized Views in Oracle |
2020 |
VLDB |
6.8561069e-05 |
| 4,113 |
Automatic Database Configuration Debugging using Retrieval-Augmented Language Models |
2025 |
SIGMOD |
6.7964307e-05 |
| 4,240 |
LEON: A New Framework for ML-Aided Query Optimization |
2023 |
VLDB |
6.7064546e-05 |
| 4,470 |
Real-time Workload Pattern Analysis for Large-scale Cloud Databases |
2023 |
VLDB |
6.5833414e-05 |
| 4,559 |
Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts |
2022 |
SIGMOD |
6.5324275e-05 |
| 4,677 |
AutoSteer: Learned Query Optimization for Any SQL Database |
2023 |
VLDB |
6.4721041e-05 |
| 4,713 |
Learned Index Benefits: Machine Learning Based Index Performance Estimation |
2022 |
VLDB |
6.4543291e-05 |
| 4,851 |
The RLR-Tree: A Reinforcement Learning Based R-Tree for Spatial Data |
2023 |
SIGMOD |
6.377837e-05 |
| 4,910 |
METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection |
2024 |
VLDB |
6.3554126e-05 |
| 4,970 |
Budget-aware Index Tuning with Reinforcement Learning |
2022 |
SIGMOD |
6.3319052e-05 |
| 5,020 |
Analyzing the Impact of Cardinality Estimation on Execution Plans in Microsoft SQL Server |
2023 |
VLDB |
6.3096708e-05 |
| 5,284 |
An Efficient Transfer Learning Based Configuration Adviser for Database Tuning |
2024 |
VLDB |
6.1963031e-05 |
| 5,316 |
How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks |
2025 |
SIGMOD |
6.1827415e-05 |
| 5,938 |
Doppler: Automated SKU Recommendation in Migrating SQL Workloads to the Cloud |
2022 |
VLDB |
5.9383582e-05 |
| 6,086 |
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 |
5.8893767e-05 |
| 6,298 |
Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective |
2024 |
VLDB |
5.8177684e-05 |
| 6,664 |
Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation |
2023 |
SIGMOD |
5.715134e-05 |
| 6,732 |
A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning |
2023 |
SIGMOD |
5.6921776e-05 |
| 6,796 |
dbET: Execution Time Distribution-based Plan Selection |
2023 |
SIGMOD |
5.6784895e-05 |