Cardinality estimation has long been grounded in statistical tools for density estimation. To
capture the rich multivariate distributions of relational tables, we propose the use of a new …

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 …

Cardinality estimation is a fundamental but long unresolved problem in query optimization.
Recently, multiple papers from different research groups consistently report that learned …

We explore the idea of using deep reinforcement learning for query optimization. The
approach is to build queries incrementally by encoding properties of subqueries using a …

Query optimizer is at the heart of the database systems. Cost-based optimizer studied in this
paper is adopted in almost all current database systems. A cost-based optimizer introduces …

Selectivity estimation-the problem of estimating the result size of queries-is a fundamental
problem in databases. Accurate estimation of query selectivity involving multiple correlated …

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 …

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 …