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 (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 …

Database and Artificial Intelligence (AI) can benefit from each other. On one hand, AI can
make database more intelligent (AI4DB). For example, traditional empirical database …

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

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

We introduce Deep Sketches, which are compact models of databases that allow us to
estimate the result sizes of SQL queries. Deep Sketches are powered by a new deep …

Recent advances in video processing utilizing deep learning primitives achieved
breakthroughs in fundamental problems in video analysis such as frame classification and …

In this paper, we investigate the possibilities of utilizing deep learning for cardinality
estimation of similarity selection. Answering this problem accurately and efficiently is …

While estimating the result size of a group-by operation on a base table is hard on its own,
the presence of selections makes this problem increasingly difficult to solve. We show that …