Cloud-native databases become increasingly important for the era of cloud computing, due
to the needs for elasticity and on-demand usage by various applications. These challenges …

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 …

Scanning and filtering over multi-dimensional tables are key operations in modern analytical
database engines. To optimize the performance of these operations, databases often create …

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 …

We study the problem of using low computational cost to automate the choices of learners
and hyperparameters for an ad-hoc training dataset and error metric, by conducting trials of …

Constructing energy-efficient database systems to reduce economic costs and
environmental impact has been studied for 10 years. With the emergence of the big data …

Filtering data based on predicates is one of the most fundamental operations for any modern
data warehouse. Techniques to accelerate the execution of filter expressions include …

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 …

Video cameras have been deployed at scale today. Driven by the breakthrough in deep
learning (DL), organizations that have deployed these cameras start to use DL-based …

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 …