Deep learning has recently become very popular on account of its incredible success in
many complex datadriven applications, including image classification and speech …

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 …

We describe a new deep learning approach to cardinality estimation. MSCN is a multi-set
convolutional network, tailored to representing relational query plans, that employs set …

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 …

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 …

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 …