Modern database management systems (DBMS) expose hundreds of configurable knobs to
control system behaviours. Determining the appropriate values for these knobs to improve …

The last decade of database research has led to the prevalence of specialized systems for
different workloads. Consequently, organizations often rely on a combination of specialized …

Recently, using automatic configuration tuning to improve the performance of modern
database management systems (DBMSs) has attracted increasing interest from the …

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 …

Tuning a database system to achieve optimal performance on a given workload is a long-
standing problem in the database community. A number of recent works have leveraged ML …

Self-tuning is a feature of autonomic databases that includes the problem of automatic
schema design. It aims at providing an optimized schema that increases the overall …

Enterprises use distributed database systems to meet the demands of mixed or hybrid
transaction/analytical processing (HTAP) workloads that contain both transactional (OLTP) …

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 …

Modern organizations manage their data with a wide variety of specialized cloud database
engines (eg, Aurora, BigQuery, etc.). However, designing and managing such infrastructures …