Cloud databases have been widely accepted and deployed due to their unique advantages,
such as high elasticity, high availability, and low cost. Many new techniques, such as …

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 …

For cardinality estimation in DBMS, building multiple local models instead of one global
model can usually improve estimation accuracy as well as reducing the effort to label large …

Cardinality estimation (CE) plays a crucial role in database optimizer. We have witnessed
the emergence of numerous learned CE models recently which can outperform traditional …

Cost estimation is of great importance in query optimization. However, traditional optimizers
compute the cost based on heuristics, sacrificing accuracy for efficiency. In recent years …

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 …

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 …

Since Gartner coined the term, Hybrid Transactional and Analytical Processing (HTAP),
numerous HTAP databases have been proposed to combine transactions with analytics in …

Selectivity estimation is a fundamental task in database management systems and has been
studied for decades. Given a SQL query, selectivity estimation returns the number of records …