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

Efficiently scheduling data processing jobs on distributed compute clusters requires complex
algorithms. Current systems use simple, generalized heuristics and ignore workload …

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 …

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 …

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 …

Similar to humans, RL agents use interactive learning to successfully obtain satisfactory
decision strategies. However, in many cases, it is desirable to learn directly from …

Data processing and analytics are fundamental and pervasive. Algorithms play a vital role in
data processing and analytics where many algorithm designs have incorporated heuristics …

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

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 …

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 …