| 20 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00058294381 |
| 89 |
Automatic Database Management System Tuning Through Large-scale Machine Learning |
2017 |
SIGMOD |
0.00035598024 |
| 238 |
Self-Driving Database Management Systems |
2017 |
CIDR |
0.00024001206 |
| 334 |
An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning |
2019 |
SIGMOD |
0.00020961385 |
| 377 |
AutoAdmin "What-if" Index Analysis Utility |
1998 |
SIGMOD |
0.00019763016 |
| 394 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00019400224 |
| 461 |
Query-based Workload Forecasting for Self-Driving Database Management Systems |
2018 |
SIGMOD |
0.00018144613 |
| 493 |
QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning |
2019 |
VLDB |
0.00017600244 |
| 511 |
Adaptive Self-Tuning Memory in DB2 |
2006 |
VLDB |
0.00017332557 |
| 560 |
Plan-Structured Deep Neural Network Models for Query Performance Prediction |
2019 |
VLDB |
0.00016570735 |
| 755 |
Automatic Physical Database Tuning: A Relaxation-based Approach |
2005 |
SIGMOD |
0.00014368592 |
| 1,280 |
AI Meets AI: Leveraging Query Executions to Improve Index Recommendations |
2019 |
SIGMOD |
0.0001144289 |
| 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 |
| 1,955 |
D-Bot: Database Diagnosis System using Large Language Models |
2024 |
VLDB |
9.4947828e-05 |
| 1,960 |
ReAcTable: Enhancing ReAct for Table Question Answering |
2024 |
VLDB |
9.4913606e-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 |
| 2,571 |
Why TPC Is Not Enough: An Analysis of the Amazon Redshift Fleet |
2024 |
VLDB |
8.4677877e-05 |
| 2,766 |
ELPIS: Graph-Based Similarity Search for Scalable Data Science |
2023 |
VLDB |
8.2159054e-05 |
| 2,859 |
Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation |
2022 |
VLDB |
8.094221e-05 |
| 2,929 |
Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction |
2022 |
VLDB |
8.0220847e-05 |
| 3,096 |
LLM-R2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency |
2025 |
VLDB |
7.8298907e-05 |
| 3,114 |
Instance-Optimized Data Layouts for Cloud Analytics Workloads |
2021 |
SIGMOD |
7.79794e-05 |
| 3,298 |
LlamaTune: Sample-Efficient DBMS Configuration Tuning |
2022 |
VLDB |
7.6094798e-05 |
| 3,475 |
Robust Query Driven Cardinality Estimation under Changing Workloads |
2023 |
VLDB |
7.4606119e-05 |
| 3,555 |
Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation |
2021 |
VLDB |
7.3766884e-05 |
| 3,556 |
LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans |
2023 |
VLDB |
7.3766003e-05 |
| 3,858 |
UDO: Universal Database Optimization using Reinforcement Learning |
2021 |
VLDB |
7.119457e-05 |
| 3,911 |
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems |
2021 |
SIGMOD |
7.0870659e-05 |
| 4,043 |
Automated Generation of Materialized Views in Oracle |
2020 |
VLDB |
7.0040078e-05 |
| 4,205 |
Combining Small Language Models and Large Language Models for Zero-Shot NL2SQL |
2024 |
VLDB |
6.898642e-05 |
| 4,342 |
Real-time Workload Pattern Analysis for Large-scale Cloud Databases |
2023 |
VLDB |
6.823998e-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 |
| 4,700 |
The RLR-Tree: A Reinforcement Learning Based R-Tree for Spatial Data |
2023 |
SIGMOD |
6.619414e-05 |
| 4,717 |
Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts |
2022 |
SIGMOD |
6.6108379e-05 |
| 4,858 |
AutoSteer: Learned Query Optimization for Any SQL Database |
2023 |
VLDB |
6.5410955e-05 |
| 4,986 |
Analyzing the Impact of Cardinality Estimation on Execution Plans in Microsoft SQL Server |
2023 |
VLDB |
6.4810456e-05 |
| 5,043 |
Budget-aware Index Tuning with Reinforcement Learning |
2022 |
SIGMOD |
6.4560989e-05 |
| 5,069 |
METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection |
2024 |
VLDB |
6.443981e-05 |
| 5,095 |
An Efficient Transfer Learning Based Configuration Adviser for Database Tuning |
2024 |
VLDB |
6.4313378e-05 |
| 5,932 |
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 |
6.099142e-05 |
| 6,092 |
Doppler: Automated SKU Recommendation in Migrating SQL Workloads to the Cloud |
2022 |
VLDB |
6.0417656e-05 |
| 6,162 |
Automatic Database Configuration Debugging using Retrieval-Augmented Language Models |
2025 |
SIGMOD |
6.0310777e-05 |
| 6,282 |
How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks |
2025 |
SIGMOD |
5.9906069e-05 |
| 6,433 |
Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective |
2024 |
VLDB |
5.9423732e-05 |
| 6,502 |
A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning |
2023 |
SIGMOD |
5.9152263e-05 |
| 6,698 |
Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation |
2023 |
SIGMOD |
5.8569921e-05 |
| 7,261 |
E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model |
2025 |
VLDB |
5.715327e-05 |