Recent efforts applying machine learning techniques to query optimization have shown few
practical gains due to substantive training overhead, inability to adapt to changes, and poor …

The typical approach for learned DBMS components is to capture the behavior by running a
representative set of queries and use the observations to train a machine learning model …

Recent research has shown that learned models can outperform state-of-the-art index
structures in size and lookup performance. While this is a very promising result, existing …

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

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 …

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 …

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 …

SkinnerDB uses reinforcement learning for reliable join ordering, exploiting an adaptive
processing engine with specialized join algorithms and data structures. It maintains no data …

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

In recent years, there has been tremendous interest in research that applies machine
learning to database systems. Being one of the most complex components of a DBMS, query …