| 183 |
Automatic Database Management System Tuning Through Large-scale Machine Learning |
2017 |
SIGMOD |
0.00036859633 |
| 339 |
OLTP-Bench: An Extensible Testbed for Benchmarking Relational Databases |
2014 |
VLDB |
0.00026895683 |
| 371 |
Self-Driving Database Management Systems |
2017 |
CIDR |
0.00025382677 |
| 634 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00018844568 |
| 704 |
Query-based Workload Forecasting for Self-Driving Database Management Systems |
2018 |
SIGMOD |
0.00017785557 |
| 796 |
SageDB: A Learned Database System |
2019 |
CIDR |
0.00016541749 |
| 876 |
Plan-Structured Deep Neural Network Models for Query Performance Prediction |
2019 |
VLDB |
0.00015660534 |
| 1,024 |
DBSherlock: A Performance Diagnostic Tool for Transactional Databases |
2016 |
SIGMOD |
0.00014601525 |
| 1,273 |
Amazon Redshift Re-invented |
2022 |
SIGMOD |
0.00012870386 |
| 1,321 |
Automated Demand-driven Resource Scaling in Relational Database-as-a-Service |
2016 |
SIGMOD |
0.00012605455 |
| 1,431 |
An Empirical Evaluation of In-Memory Multi-Version Concurrency Control |
2017 |
VLDB |
0.00012021808 |
| 1,443 |
Compressing SQL Workloads |
2002 |
SIGMOD |
0.00011944621 |
| 1,816 |
An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems |
2021 |
VLDB |
0.00010438512 |
| 2,050 |
Automatically Indexing Millions of Databases in Microsoft Azure SQL Database |
2019 |
SIGMOD |
9.6883066e-05 |
| 2,239 |
Performance and Resource Modeling in Highly-Concurrent OLTP Workloads |
2013 |
SIGMOD |
9.2163469e-05 |
| 2,309 |
On Predictive Modeling for Optimizing Transaction Execution in Parallel OLTP Systems |
2012 |
VLDB |
9.0630462e-05 |
| 2,386 |
Oracle AutoML: A Fast and Predictive AutoML Pipeline |
2020 |
VLDB |
8.9167446e-05 |
| 2,996 |
Data Generation for Application-Specific Benchmarking |
2011 |
VLDB |
7.7580926e-05 |
| 3,076 |
Explore-by-Example: An Automatic Query Steering Framework for Interactive Data Exploration |
2014 |
SIGMOD |
7.6063803e-05 |
| 3,102 |
Oracle Database Replay |
2008 |
SIGMOD |
7.5604346e-05 |
| 3,144 |
Active Learning for ML Enhanced Database Systems |
2020 |
SIGMOD |
7.4844943e-05 |
| 3,241 |
A Learned Query Rewrite System using Monte Carlo Tree Search |
2022 |
VLDB |
7.32744e-05 |
| 3,838 |
The evolution of Amazon Redshift (extended abstract) |
2021 |
VLDB |
6.7113252e-05 |
| 4,151 |
openGauss: An Autonomous Database System |
2021 |
VLDB |
6.4020605e-05 |
| 4,216 |
Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation |
2021 |
VLDB |
6.3448176e-05 |
| 4,227 |
Cosine: A Cloud-Cost Optimized Self-Designing Key-Value Storage Engine |
2022 |
VLDB |
6.3381409e-05 |
| 4,469 |
Comprehensive and Efficient Workload Compression |
2021 |
VLDB |
6.1535623e-05 |
| 4,587 |
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems |
2021 |
SIGMOD |
6.0594195e-05 |
| 4,730 |
UDO: Universal Database Optimization using Reinforcement Learning |
2021 |
VLDB |
5.9604983e-05 |
| 5,944 |
SAM: Database Generation from Query Workloads with Supervised Autoregressive Models |
2022 |
SIGMOD |
5.2583712e-05 |
| 6,364 |
ISUM: Efficiently Compressing Large and Complex Workloads for Scalable Index Tuning |
2022 |
SIGMOD |
5.0895007e-05 |
| 6,507 |
Expand your Training Limits! Generating Training Data for ML-based Data Management |
2021 |
SIGMOD |
5.0273414e-05 |
| 7,760 |
Dscaler: Synthetically Scaling A Given Relational Database |
2016 |
VLDB |
4.6548456e-05 |
| 7,895 |
HYDRA: A Dynamic Big Data Regenerator |
2018 |
VLDB |
4.6192674e-05 |
| 8,036 |
Tastes Great! Less Filling! High Performance and Accurate Training Data Collection for Self-Driving Database Management Systems |
2022 |
SIGMOD |
4.5965825e-05 |
| 9,297 |
Farm Your ML-based Query Optimizer's Food! - Human-Guided Training Data Generation - |
2022 |
CIDR |
4.357774e-05 |
| 9,834 |
Is Data Management the Beating Heart of AI Systems? |
2022 |
SIGMOD |
4.2706095e-05 |
| 9,835 |
Projection-Compliant Database Generation |
2022 |
VLDB |
4.2706095e-05 |