Query optimization is a key component in database management systems (DBMS) and
distributed data processing platforms. Recent research in the database community …

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

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

Recent work in database query optimization has used complex machine learning strategies,
such as customized reinforcement learning schemes. Surprisingly, we show that LLM …

Query performance (eg, execution time) prediction is a critical component of modern
DBMSes. As a pioneering cloud data warehouse, Amazon Redshift relies on an accurate …

Various works have utilized deep learning to address the query optimization problem in
database system. They either learn to construct plans from scratch in a bottom-up manner or …

Query optimization has become a research area where classical algorithms are being
challenged by machine learning algorithms. At the same time, recent trends in learned query …

Over the last decades, various cost-based optimizers have been proposed to generate
optimal plans for SQL queries. These optimizers are key to achieving good performance in …

This dissertation explores adaptive optimizations for database systems, which take runtime
information like access patterns or hardware utilization into account. We present adaptive …

SQL query optimization aims to choose an optimal Query Execution Plan (QEP) for a query.
The existing optimizers usually choose the plan with the minimal execution cost. However, in …