This survey presents recent progress on using machine learning techniques to improve
query optimizers in database systems. Centering around a generic paradigm of learned …

Learning representations for query plans play a pivotal role in machine learning-based
query optimizers of database management systems. To this end, particular model …

Query optimization aims to select a query execution plan among all query paths for a given
query. The query optimization of traditional relational database management systems …

Query optimization is of great importance for the performance of a database, in which
cardinality and cost estimation have a pivotal role. To enable accurate cardinality and cost …

Traditionally, query optimizers rely on cost models to choose the best execution plan from
several candidates, making precise cost estimates critical for efficient query execution. In …

In recent years, there has been a growing interest in using machine learning (ML) in query
optimization to select more efficient plans. Existing learning-based query optimizers use …

Learning representations for query plans play a pivotal role in machine learning-based
query optimizers of database management systems. To this end, particular model …

In recent times, learned query optimizer has becoming a hot research topic in learned
databases. It serves as the most suitable experimental plots for utilizing numerous machine …

Query cost estimation is a classical task for database management. Recently, researchers
have applied AI-driven methods to implement query cost estimation for achieving high …

Learning representations for query plans play a pivotal role in machine learning-based
query optimizers of database management sys-tems. To this end, particular model …