Query optimization in relational database management systems (DBMSs) is critical for fast
query processing. The query optimizer relies on precise selectivity and cost estimates to …

The traditional and well-established cost-based query optimizer approach enumerates
different execution plans for each query, assesses each plan with costs, and selects the plan …

The broadening adoption of machine learning in the enterprise is increasing the pressure for
strict governance and cost-effective performance, in particular for the common and …

Query optimizers in relational database management systems (RDBMSs) search for
execution plans expected to be optimal for a given queries. They use parameter estimates …

Some recent works have shown the advantages of reinforcement learning (RL) based
learned query optimizers. These works often use the cost (ie, the estimation of cost model) or …

Query optimizers, crucial components of relational database management systems, are
responsible for generating efficient query execution plans. Despite many advances in the …

Relational database management systems (RDBMSs) employ query optimizers to search for
execution plans deemed optimal for specific queries. Classical optimizers rely on inaccurate …

HRDBMS is a novel distributed relational database that uses a hybrid model combining the
best of traditional distributed relational databases and Big Data analytics platforms such as …

Query optimization has long been a fundamental yet challenging topic in the database field.
With the prosperity of machine learning (ML), some recent works have shown the …

In recent years, there has been tremendous interest in research that applies machine
learning to database systems. Being one of the most complex components of a DBMS, query …