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

In recent studies, machine learning techniques have been employed to support or enhance
cost-based query optimizers in DBMS. Although these approaches have shown superiority …

The efficiency of query processing in the Spark SQL big data processing engine is
significantly affected by execution plans and allocated resources. However, existing cost …

Cardinality estimation (CardEst) is essential for optimizing query execution plans. Recent
ML-based CardEst methods achieve high accuracy but face deployment challenges due to …

Databases are increasingly embracing AI to provide autonomous system optimization and
intelligent in-database analytics, aiming to relieve end-user burdens across various industry …

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 …

We present LimeQO, a learned steering query optimizer based on linear methods, such as
matrix completion, for repetitive workloads. LimeQO can forgo expensive neural networks by …

Native distributed databases, crucial for scalable applications, offer transactional and
analytical prowess but face data intricacies and network challenges. Under the CAP …