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 driven cardinality estimation models learn from a historical log of queries. They are
lightweight, having low storage requirements, fast inference and training, and are easily …

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 a fundamental and critical problem in databases. Recently, many
estimators based on deep learning have been proposed to solve this problem and they have …

The last decade of database research has led to the prevalence of specialized systems for
different workloads. Consequently, organizations often rely on a combination of specialized …

Query optimization has long been a fundamental yet challenging topic in the database field.
With the prosperity of machine learning (ML), some recent works have shown the …

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

For efficient query processing, DBMS query optimizers have for decades relied on delicate
cardinality estimation methods. In this work, we propose an Attention-based LEarned …

Most existing parametric query optimization (PQO) techniques rely on traditional query
optimizer cost models, which are often inaccurate and result in suboptimal query …

Recently, the database management system (DBMS) community has witnessed the power of
machine learning (ML) solutions for DBMS tasks. Despite their promising performance …