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 …

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 …

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

We study the problem of optimizing subgraph queries using the new worst-case optimal join
plans. Worst-case optimal plans evaluate queries by matching one query vertex at a time …

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 …

Machine learning (ML) has become the de-facto approach for various scientific domains
such as computer vision and natural language processing. Despite recent breakthroughs …

Join ordering and join processing has a huge impact on query execution and can easily
affect the query response time by orders of magnitude. In particular, when joins are …

Cardinality estimation is a fundamental problem in database systems. To capture the rich
joint data distributions of a relational table, most of the existing work either uses data as …