The performance optimization of database systems has been widely studied for years. From
the perspective of the operation and maintenance personnel, it mainly includes three topics …

Configuration knobs of database systems are essential to achieve high throughput and low
latency. Recently, automatic tuning systems using machine learning methods (ML) have …

Knob tuning plays an important role in database optimization, which tunes knob settings to
optimize the database performance or improve resource utilization. However, there are …

Learned databases, or AI4DB techniques, have rapidly developed in the last decade.
Deploying machine learning (ML) and AI4DB algorithms into actual databases is the gold …

Recently, using machine learning for performance tuning of cloud database (CDB) service
has shown great potentials. However, facing personalized requirements such as various …

Database management systems (DBMSs) are notoriously difficult to deploy and administer.
Self-driving DBMSs seek to remove these impediments by managing themselves …

Many database optimization problems, eg, slow SQL diagnosis, database testing, optimizer
tuning, require a large volume of SQL queries. Due to privacy issues, it is hard to obtain real …

Modern data systems are typically both complex and general-purpose. They are complex
because of the numerous internal knobs and parameters that users need to manually tune in …

Existing machine learning (ML) approaches to automatically optimize database
management systems (DBMSs) only target a single configuration space at a time (eg, knobs …

Machine learning techniques have been proposed to optimize data management in recent
years. Compared with traditional empirical data management, learning-based methods …