| 183 |
Automatic Database Management System Tuning Through Large-scale Machine Learning |
2017 |
SIGMOD |
0.00036721403 |
| 340 |
OLTP-Bench: An Extensible Testbed for Benchmarking Relational Databases |
2014 |
VLDB |
0.00026841628 |
| 359 |
Self-Driving Database Management Systems |
2017 |
CIDR |
0.0002592783 |
| 640 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00018759152 |
| 716 |
Query-based Workload Forecasting for Self-Driving Database Management Systems |
2018 |
SIGMOD |
0.00017723171 |
| 801 |
SageDB: A Learned Database System |
2019 |
CIDR |
0.00016505496 |
| 884 |
Plan-Structured Deep Neural Network Models for Query Performance Prediction |
2019 |
VLDB |
0.00015654004 |
| 1,022 |
DBSherlock: A Performance Diagnostic Tool for Transactional Databases |
2016 |
SIGMOD |
0.00014614917 |
| 1,284 |
Amazon Redshift Re-invented |
2022 |
SIGMOD |
0.00012837822 |
| 1,322 |
Automated Demand-driven Resource Scaling in Relational Database-as-a-Service |
2016 |
SIGMOD |
0.00012610912 |
| 1,432 |
An Empirical Evaluation of In-Memory Multi-Version Concurrency Control |
2017 |
VLDB |
0.00012017544 |
| 1,443 |
Compressing SQL Workloads |
2002 |
SIGMOD |
0.00011947004 |
| 1,827 |
An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems |
2021 |
VLDB |
0.00010390548 |
| 2,047 |
Automatically Indexing Millions of Databases in Microsoft Azure SQL Database |
2019 |
SIGMOD |
9.6920209e-05 |
| 2,230 |
Performance and Resource Modeling in Highly-Concurrent OLTP Workloads |
2013 |
SIGMOD |
9.2322426e-05 |
| 2,307 |
On Predictive Modeling for Optimizing Transaction Execution in Parallel OLTP Systems |
2012 |
VLDB |
9.0599752e-05 |
| 2,384 |
Oracle AutoML: A Fast and Predictive AutoML Pipeline |
2020 |
VLDB |
8.925354e-05 |
| 2,994 |
Data Generation for Application-Specific Benchmarking |
2011 |
VLDB |
7.761114e-05 |
| 3,070 |
Explore-by-Example: An Automatic Query Steering Framework for Interactive Data Exploration |
2014 |
SIGMOD |
7.6137064e-05 |
| 3,098 |
Oracle Database Replay |
2008 |
SIGMOD |
7.5646066e-05 |
| 3,142 |
Active Learning for ML Enhanced Database Systems |
2020 |
SIGMOD |
7.4815444e-05 |
| 3,248 |
A Learned Query Rewrite System using Monte Carlo Tree Search |
2022 |
VLDB |
7.3258782e-05 |
| 3,844 |
The evolution of Amazon Redshift (extended abstract) |
2021 |
VLDB |
6.7076451e-05 |
| 4,152 |
openGauss: An Autonomous Database System |
2021 |
VLDB |
6.4060406e-05 |
| 4,227 |
Cosine: A Cloud-Cost Optimized Self-Designing Key-Value Storage Engine |
2022 |
VLDB |
6.3434324e-05 |
| 4,240 |
Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation |
2021 |
VLDB |
6.3318228e-05 |
| 4,468 |
Comprehensive and Efficient Workload Compression |
2021 |
VLDB |
6.1584035e-05 |
| 4,590 |
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems |
2021 |
SIGMOD |
6.0620053e-05 |
| 4,913 |
UDO: Universal Database Optimization using Reinforcement Learning |
2021 |
VLDB |
5.8316231e-05 |
| 5,942 |
SAM: Database Generation from Query Workloads with Supervised Autoregressive Models |
2022 |
SIGMOD |
5.2634242e-05 |
| 6,366 |
ISUM: Efficiently Compressing Large and Complex Workloads for Scalable Index Tuning |
2022 |
SIGMOD |
5.0943443e-05 |
| 6,519 |
Expand your Training Limits! Generating Training Data for ML-based Data Management |
2021 |
SIGMOD |
5.0316686e-05 |
| 7,759 |
Dscaler: Synthetically Scaling A Given Relational Database |
2016 |
VLDB |
4.6593145e-05 |
| 7,895 |
HYDRA: A Dynamic Big Data Regenerator |
2018 |
VLDB |
4.623701e-05 |
| 8,082 |
Tastes Great! Less Filling! High Performance and Accurate Training Data Collection for Self-Driving Database Management Systems |
2022 |
SIGMOD |
4.5905454e-05 |
| 9,292 |
Farm Your ML-based Query Optimizer's Food! - Human-Guided Training Data Generation - |
2022 |
CIDR |
4.3619543e-05 |
| 9,835 |
Is Data Management the Beating Heart of AI Systems? |
2022 |
SIGMOD |
4.2747054e-05 |
| 9,836 |
Projection-Compliant Database Generation |
2022 |
VLDB |
4.2747054e-05 |