| 160 |
Automated Selection of Materialized Views and Indexes for SQL Databases |
2000 |
VLDB |
0.00040053897 |
| 181 |
LEO - DB2's LEarning Optimizer |
2001 |
VLDB |
0.00036970794 |
| 183 |
Automatic Database Management System Tuning Through Large-scale Machine Learning |
2017 |
SIGMOD |
0.00036859633 |
| 237 |
An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server |
1997 |
VLDB |
0.00031727601 |
| 258 |
DB2 Design Advisor: Integrated Automatic Physical Database Design |
2004 |
VLDB |
0.00030196528 |
| 283 |
Integrating Vertical and Horizontal Partitioning into Automated Physical Database Design |
2004 |
SIGMOD |
0.00029024583 |
| 285 |
Automating Physical Database Design in a Parallel Database |
2002 |
SIGMOD |
0.00028978423 |
| 371 |
Self-Driving Database Management Systems |
2017 |
CIDR |
0.00025382677 |
| 407 |
Database Cracking |
2007 |
CIDR |
0.00023941779 |
| 423 |
Tuning Database Configuration Parameters with iTuned |
2009 |
VLDB |
0.00023628474 |
| 510 |
An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning |
2019 |
SIGMOD |
0.00021420477 |
| 528 |
Rethinking Database System Architecture: Towards a Self-tuning RISC-style Database System |
2000 |
VLDB |
0.0002085799 |
| 634 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00018844568 |
| 661 |
Adaptive Self-Tuning Memory in DB2 |
2006 |
VLDB |
0.00018488168 |
| 679 |
Skew-Aware Automatic Database Partitioning in Shared-Nothing, Parallel OLTP Systems |
2012 |
SIGMOD |
0.00018211621 |
| 704 |
Query-based Workload Forecasting for Self-Driving Database Management Systems |
2018 |
SIGMOD |
0.00017785557 |
| 760 |
Automatic Performance Diagnosis and Tuning in Oracle |
2005 |
CIDR |
0.00017003914 |
| 779 |
QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning |
2019 |
VLDB |
0.00016719473 |
| 841 |
Self-tuning Database Technology and Information Services: from Wishful Thinking to Viable Engineering |
2002 |
VLDB |
0.00015987128 |
| 845 |
Index Selection in a Self-Adaptive Data Base Management System |
1976 |
SIGMOD |
0.00015953779 |
| 876 |
Plan-Structured Deep Neural Network Models for Query Performance Prediction |
2019 |
VLDB |
0.00015660534 |
| 1,321 |
Automated Demand-driven Resource Scaling in Relational Database-as-a-Service |
2016 |
SIGMOD |
0.00012605455 |
| 1,443 |
Compressing SQL Workloads |
2002 |
SIGMOD |
0.00011944621 |
| 1,697 |
Bridging the Archipelago between Row-Stores and Column-Stores for Hybrid Workloads |
2016 |
SIGMOD |
0.00010859294 |
| 1,801 |
H2O: A Hands-free Adaptive Store |
2014 |
SIGMOD |
0.00010485628 |
| 1,810 |
SQL Memory Management in Oracle9i |
2002 |
VLDB |
0.00010471365 |
| 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 |
| 2,410 |
Automated Partitioning Design in Parallel Database Systems |
2011 |
SIGMOD |
8.8643562e-05 |
| 3,144 |
Active Learning for ML Enhanced Database Systems |
2020 |
SIGMOD |
7.4844943e-05 |
| 3,580 |
Query Performance Prediction for Concurrent Queries using Graph Embedding |
2020 |
VLDB |
6.9460425e-05 |
| 3,725 |
Estimating Cardinalities with Deep Sketches |
2019 |
SIGMOD |
6.8117015e-05 |
| 3,905 |
A Demonstration of the OtterTune Automatic Database Management System Tuning Service |
2018 |
VLDB |
6.6430079e-05 |
| 4,235 |
CGPTuner: a Contextual Gaussian Process Bandit Approach for the Automatic Tuning of IT Configurations Under Varying Workload Conditions |
2021 |
VLDB |
6.3297728e-05 |
| 4,587 |
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems |
2021 |
SIGMOD |
6.0594195e-05 |
| 8,180 |
Demonstrating UDO: A Unified Approach for Optimizing Transaction Code, Physical Design, and System Parameters via Reinforcement Learning |
2021 |
SIGMOD |
4.5627116e-05 |