Cardinality estimation is a critical component and a longstanding challenge in modern data
warehouses. ByteHouse, ByteDance's cloud-native engine for extensive data analysis in …

Cardinality estimation is a critical component and a longstanding challenge in modern data
warehouses. ByteHouse, ByteDance's cloud-native engine for big data analysis in exabyte …

In database systems, accurate cardinality estimation is a cornerstone of effective query
optimization. In this context, estimators that use machine learning have shown significant …

Query optimizers rely on accurate cardinality estimates to produce good execution plans.
Despite decades of research, existing cardinality estimators are inaccurate for complex …

Learned cardinality estimators have shown remarkable improvements in estimation
accuracy by exploiting machine learning techniques, yet suffer from inefficiency or sub …

Recent learned cardinality estimation (CE) models are vulnerable when query predicates or
the underlying datasets drift from what the models were trained upon. We propose a system …

Cardinality estimation is a fundamental problem in database systems. To capture the rich
joint data distributions of a relational table, most of the existing work either uses data as …

Cardinality Estimation (CE) is a fundamental but critical problem in DBMS query
optimization, while deep learning techniques have made significant breakthroughs in the …

Cardinality estimation is one of the most fundamental and challenging problems in query
optimization. Neither classical nor learning-based methods yield satisfactory performance …

Cardinality estimation (CardEst) is an essential component in query optimizers and a
fundamental problem in DBMS. A desired CardEst method should attain good algorithm …