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 …

Query optimization is one of the most challenging problems in database systems. Despite
the progress made over the past decades, query optimizers remain extremely complex …

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 work on" learned indexes" has changed the way we look at the decades-old field of
DBMS indexing. The key idea is that indexes can be thought of as" models" that predict the …

Cost and cardinality estimation is vital to query optimizer, which can guide the plan
selection. However traditional empirical cost and cardinality estimation techniques cannot …

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 …

Exhaustive enumeration of all possible join orders is often avoided, and most optimizers
leverage heuristics to prune the search space. The design and implementation of heuristics …

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

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 (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 …