| 158 |
Automated Selection of Materialized Views and Indexes for SQL Databases |
2000 |
VLDB |
0.00040071492 |
| 182 |
LEO - DB2's LEarning Optimizer |
2001 |
VLDB |
0.00036962631 |
| 183 |
Automatic Database Management System Tuning Through Large-scale Machine Learning |
2017 |
SIGMOD |
0.00036721403 |
| 237 |
An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server |
1997 |
VLDB |
0.00031726304 |
| 258 |
DB2 Design Advisor: Integrated Automatic Physical Database Design |
2004 |
VLDB |
0.0003022091 |
| 285 |
Automating Physical Database Design in a Parallel Database |
2002 |
SIGMOD |
0.0002899128 |
| 286 |
Integrating Vertical and Horizontal Partitioning into Automated Physical Database Design |
2004 |
SIGMOD |
0.00028990057 |
| 359 |
Self-Driving Database Management Systems |
2017 |
CIDR |
0.0002592783 |
| 408 |
Database Cracking |
2007 |
CIDR |
0.00023953844 |
| 424 |
Tuning Database Configuration Parameters with iTuned |
2009 |
VLDB |
0.00023616398 |
| 514 |
An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning |
2019 |
SIGMOD |
0.0002124895 |
| 527 |
Rethinking Database System Architecture: Towards a Self-tuning RISC-style Database System |
2000 |
VLDB |
0.00020868847 |
| 640 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00018759152 |
| 663 |
Adaptive Self-Tuning Memory in DB2 |
2006 |
VLDB |
0.00018469455 |
| 679 |
Skew-Aware Automatic Database Partitioning in Shared-Nothing, Parallel OLTP Systems |
2012 |
SIGMOD |
0.00018215154 |
| 716 |
Query-based Workload Forecasting for Self-Driving Database Management Systems |
2018 |
SIGMOD |
0.00017723171 |
| 765 |
Automatic Performance Diagnosis and Tuning in Oracle |
2005 |
CIDR |
0.00017016449 |
| 782 |
QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning |
2019 |
VLDB |
0.00016729063 |
| 846 |
Self-tuning Database Technology and Information Services: from Wishful Thinking to Viable Engineering |
2002 |
VLDB |
0.00015997985 |
| 874 |
Index Selection in a Self-Adaptive Data Base Management System |
1976 |
SIGMOD |
0.00015728533 |
| 884 |
Plan-Structured Deep Neural Network Models for Query Performance Prediction |
2019 |
VLDB |
0.00015654004 |
| 1,322 |
Automated Demand-driven Resource Scaling in Relational Database-as-a-Service |
2016 |
SIGMOD |
0.00012610912 |
| 1,443 |
Compressing SQL Workloads |
2002 |
SIGMOD |
0.00011947004 |
| 1,700 |
Bridging the Archipelago between Row-Stores and Column-Stores for Hybrid Workloads |
2016 |
SIGMOD |
0.00010858865 |
| 1,807 |
H2O: A Hands-free Adaptive Store |
2014 |
SIGMOD |
0.00010487796 |
| 1,810 |
SQL Memory Management in Oracle9i |
2002 |
VLDB |
0.0001047003 |
| 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 |
| 2,413 |
Automated Partitioning Design in Parallel Database Systems |
2011 |
SIGMOD |
8.8672223e-05 |
| 3,142 |
Active Learning for ML Enhanced Database Systems |
2020 |
SIGMOD |
7.4815444e-05 |
| 3,580 |
Query Performance Prediction for Concurrent Queries using Graph Embedding |
2020 |
VLDB |
6.9500996e-05 |
| 3,725 |
Estimating Cardinalities with Deep Sketches |
2019 |
SIGMOD |
6.8170734e-05 |
| 3,914 |
A Demonstration of the OtterTune Automatic Database Management System Tuning Service |
2018 |
VLDB |
6.6339644e-05 |
| 4,265 |
CGPTuner: a Contextual Gaussian Process Bandit Approach for the Automatic Tuning of IT Configurations Under Varying Workload Conditions |
2021 |
VLDB |
6.3097793e-05 |
| 4,590 |
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems |
2021 |
SIGMOD |
6.0620053e-05 |
| 8,180 |
Demonstrating UDO: A Unified Approach for Optimizing Transaction Code, Physical Design, and System Parameters via Reinforcement Learning |
2021 |
SIGMOD |
4.5663204e-05 |