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

Cardinality estimation (CardEst) plays a significant role in generating high-quality query
plans for a query optimizer in DBMS. In the last decade, an increasing number of advanced …

Query optimizers rely on accurate cardinality estimation (CardEst) to produce good
execution plans. The core problem of CardEst is how to model the rich joint distribution of …

Learned cardinality estimation (CE) has recently gained significant attention for replacing
long-studied traditional CE with machine learning, especially for deep learning. However …

Cardinality estimation is one of the most important problems in query optimization. Recently,
machine learning based techniques have been proposed to effectively estimate cardinality …

Cardinality estimation is core to the query optimizers of DBMSs. Non-learned methods,
especially based on histograms and samplings, have been widely used in commercial and …

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 …

Previous approaches to learned cardinality estimation have focused on improving average
estimation error, but not all estimates matter equally. Since learned models inevitably make …

Data scientists often develop data sets for analysis by drawing upon sources of data
available to them. A major challenge is to ensure that the data set used for analysis has an …

In the era of big data, computing exact answers to analytical queries becomes prohibitively
expensive. This greatly increases the value of approaches that can compute efficiently …