CardEst Method

Cardinality estimation (CardEst) aims to accurately predict the number of rows resulting from database queries, a crucial task for optimizing query execution plans. Recent research focuses on developing machine learning-based methods, particularly those leveraging pretrained models and adaptable frameworks that can handle complex join queries and diverse database structures, overcoming limitations of traditional approaches. These advancements improve query optimization efficiency and accuracy across various database systems, leading to faster query processing and better resource utilization. The development of more robust and adaptable CardEst methods is significantly impacting database management system performance and design.