| 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 |
| 157 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00028782395 |
| 223 |
OLTP-Bench: An Extensible Testbed for Benchmarking Relational Databases |
2014 |
VLDB |
0.000243333 |
| 238 |
Self-Driving Database Management Systems |
2017 |
CIDR |
0.00024001206 |
| 328 |
DeepDB: Learn from Data, not from Queries! |
2020 |
VLDB |
0.00021121613 |
| 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 |
| 452 |
Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources |
2018 |
SIGMOD |
0.00018289051 |
| 461 |
Query-based Workload Forecasting for Self-Driving Database Management Systems |
2018 |
SIGMOD |
0.00018144613 |
| 492 |
Database Tuning Advisor for Microsoft SQL Server 2005 |
2004 |
VLDB |
0.00017612998 |
| 493 |
QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning |
2019 |
VLDB |
0.00017600244 |
| 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 |
| 1,324 |
An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems |
2021 |
VLDB |
0.0001126122 |
| 1,371 |
Balsa: Learning a Query Optimizer Without Expert Demonstrations |
2022 |
SIGMOD |
0.00011100806 |
| 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 |
| 1,528 |
Automatically Indexing Millions of Databases in Microsoft Azure SQL Database |
2019 |
SIGMOD |
0.00010545806 |
| 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 |
| 2,506 |
Lero: A Learning-to-Rank Query Optimizer |
2023 |
VLDB |
8.5603022e-05 |
| 2,682 |
A Learned Query Rewrite System using Monte Carlo Tree Search |
2022 |
VLDB |
8.3248096e-05 |
| 2,688 |
Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation |
2022 |
VLDB |
8.3172831e-05 |
| 2,781 |
Query Performance Prediction for Concurrent Queries using Graph Embedding |
2020 |
VLDB |
8.1921284e-05 |
| 2,859 |
Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation |
2022 |
VLDB |
8.094221e-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 |
| 3,430 |
CGPTuner: a Contextual Gaussian Process Bandit Approach for the Automatic Tuning of IT Configurations Under Varying Workload Conditions |
2021 |
VLDB |
7.4959348e-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 |
| 3,825 |
The Case for a Learned Sorting Algorithm |
2020 |
SIGMOD |
7.1511577e-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 |
| 4,107 |
Proteus: A Self-Designing Range Filter |
2022 |
SIGMOD |
6.9589272e-05 |
| 4,342 |
Real-time Workload Pattern Analysis for Large-scale Cloud Databases |
2023 |
VLDB |
6.823998e-05 |
| 4,581 |
Kepler: Robust Learning for Faster Parametric Query Optimization |
2023 |
SIGMOD |
6.6913226e-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 |
| 4,858 |
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 |
| 5,009 |
Database Workload Characterization with Query Plan Encoders |
2022 |
VLDB |
6.4710516e-05 |
| 5,043 |
Budget-aware Index Tuning with Reinforcement Learning |
2022 |
SIGMOD |
6.4560989e-05 |
| 5,095 |
An Efficient Transfer Learning Based Configuration Adviser for Database Tuning |
2024 |
VLDB |
6.4313378e-05 |
| 5,206 |
Stage: Query Execution Time Prediction in Amazon Redshift |
2024 |
SIGMOD |
6.3868465e-05 |
| 5,214 |
FASTgres: Making Learned Query Optimizer Hinting Effective |
2023 |
VLDB |
6.3840792e-05 |
| 5,366 |
Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing |
2022 |
VLDB |
6.3169865e-05 |