| 15 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00061066921 |
| 78 |
Automatic Database Management System Tuning Through Large-scale Machine Learning |
2017 |
SIGMOD |
0.00036684414 |
| 224 |
Self-Driving Database Management Systems |
2017 |
CIDR |
0.00024013745 |
| 314 |
An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning |
2019 |
SIGMOD |
0.00021282642 |
| 362 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00019989474 |
| 378 |
AutoAdmin "What-if" Index Analysis Utility |
1998 |
SIGMOD |
0.00019549382 |
| 437 |
QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning |
2019 |
VLDB |
0.00018315867 |
| 460 |
Query-based Workload Forecasting for Self-Driving Database Management Systems |
2018 |
SIGMOD |
0.00017842695 |
| 508 |
Adaptive Self-Tuning Memory in DB2 |
2006 |
VLDB |
0.00017089907 |
| 560 |
Plan-Structured Deep Neural Network Models for Query Performance Prediction |
2019 |
VLDB |
0.00016403151 |
| 751 |
Automatic Physical Database Tuning: A Relaxation-based Approach |
2005 |
SIGMOD |
0.0001425375 |
| 1,279 |
AI Meets AI: Leveraging Query Executions to Improve Index Recommendations |
2019 |
SIGMOD |
0.00011226878 |
| 1,397 |
Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms |
2020 |
VLDB |
0.00010789242 |
| 1,515 |
DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems |
2021 |
VLDB |
0.00010417728 |
| 1,658 |
D-Bot: Database Diagnosis System using Large Language Models |
2024 |
VLDB |
9.9642078e-05 |
| 1,683 |
ReAcTable: Enhancing ReAct for Table Question Answering |
2024 |
VLDB |
9.8822754e-05 |
| 1,839 |
Why TPC Is Not Enough: An Analysis of the Amazon Redshift Fleet |
2024 |
VLDB |
9.5304799e-05 |
| 2,039 |
LLM-R^2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency |
2025 |
VLDB |
9.1493268e-05 |
| 2,250 |
QueryFormer: A Tree Transformer Model for Query Plan Representation |
2022 |
VLDB |
8.7533306e-05 |
| 2,265 |
ELPIS: Graph-Based Similarity Search for Scalable Data Science |
2023 |
VLDB |
8.7238222e-05 |
| 2,275 |
Active Learning for ML Enhanced Database Systems |
2020 |
SIGMOD |
8.7090584e-05 |
| 2,765 |
Instance-Optimized Data Layouts for Cloud Analytics Workloads |
2021 |
SIGMOD |
8.0439015e-05 |
| 2,772 |
Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation |
2022 |
VLDB |
8.035288e-05 |
| 2,885 |
Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction |
2022 |
VLDB |
7.9094988e-05 |
| 3,051 |
LlamaTune: Sample-Efficient DBMS Configuration Tuning |
2022 |
VLDB |
7.7055931e-05 |
| 3,327 |
Robust Query Driven Cardinality Estimation under Changing Workloads |
2023 |
VLDB |
7.4207879e-05 |
| 3,487 |
LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans |
2023 |
VLDB |
7.263041e-05 |
| 3,489 |
Combining Small Language Models and Large Language Models for Zero-Shot NL2SQL |
2024 |
VLDB |
7.2627807e-05 |
| 3,590 |
Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation |
2021 |
VLDB |
7.1865343e-05 |
| 3,645 |
UDO: Universal Database Optimization using Reinforcement Learning |
2021 |
VLDB |
7.1397796e-05 |
| 3,965 |
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems |
2021 |
SIGMOD |
6.8918628e-05 |
| 4,007 |
Automated Generation of Materialized Views in Oracle |
2020 |
VLDB |
6.8592987e-05 |
| 4,114 |
Automatic Database Configuration Debugging using Retrieval-Augmented Language Models |
2025 |
SIGMOD |
6.7971182e-05 |
| 4,258 |
LEON: A New Framework for ML-Aided Query Optimization |
2023 |
VLDB |
6.6994722e-05 |
| 4,468 |
Real-time Workload Pattern Analysis for Large-scale Cloud Databases |
2023 |
VLDB |
6.5863349e-05 |
| 4,563 |
Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts |
2022 |
SIGMOD |
6.5320994e-05 |
| 4,683 |
AutoSteer: Learned Query Optimization for Any SQL Database |
2023 |
VLDB |
6.4716143e-05 |
| 4,711 |
Learned Index Benefits: Machine Learning Based Index Performance Estimation |
2022 |
VLDB |
6.4573842e-05 |
| 4,850 |
The RLR-Tree: A Reinforcement Learning Based R-Tree for Spatial Data |
2023 |
SIGMOD |
6.3808017e-05 |
| 4,909 |
METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection |
2024 |
VLDB |
6.3584226e-05 |
| 4,968 |
Budget-aware Index Tuning with Reinforcement Learning |
2022 |
SIGMOD |
6.3348803e-05 |
| 5,022 |
Analyzing the Impact of Cardinality Estimation on Execution Plans in Microsoft SQL Server |
2023 |
VLDB |
6.3100988e-05 |
| 5,286 |
An Efficient Transfer Learning Based Configuration Adviser for Database Tuning |
2024 |
VLDB |
6.1971399e-05 |
| 5,683 |
How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks |
2025 |
SIGMOD |
6.0392183e-05 |
| 5,938 |
Doppler: Automated SKU Recommendation in Migrating SQL Workloads to the Cloud |
2022 |
VLDB |
5.9411704e-05 |
| 6,085 |
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 |
5.892166e-05 |
| 6,308 |
Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective |
2024 |
VLDB |
5.8177833e-05 |
| 6,660 |
Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation |
2023 |
SIGMOD |
5.7178404e-05 |
| 6,726 |
A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning |
2023 |
SIGMOD |
5.6948731e-05 |
| 6,791 |
dbET: Execution Time Distribution-based Plan Selection |
2023 |
SIGMOD |
5.6811782e-05 |