The last decade of database research has led to the prevalence of specialized systems for
different workloads. Consequently, organizations often rely on a combination of specialized …

Database management systems (DBMSs) are notoriously difficult to deploy and administer.
Self-driving DBMSs seek to remove these impediments by managing themselves …

Modern data systems are typically both complex and general-purpose. They are complex
because of the numerous internal knobs and parameters that users need to manually tune in …

Two decades ago, one of us co-authored a paper commenting on the previous 40 years of
data modelling research and development [188]. That paper demonstrated that the relational …

Microsoft's internal big-data infrastructure is one of the largest in the world---with over 300k
machines running billions of tasks from over 0.6 M daily jobs. Operating this infrastructure is …

Modern organizations manage their data with a wide variety of specialized cloud database
engines (eg, Aurora, BigQuery, etc.). However, designing and managing such infrastructures …

Thanks to the rapid advances in artificial intelligence, a brand new venue for database
performance optimization is through deep neural networks and the reinforcement learning …

Efficiently managing and querying sliding windows is a key component in stream processing
systems. Conventional index structures such as the B+ Tree are not efficient for handling a …

Recently using machine learning (ML) based techniques to optimize the performance of
modern database management systems (DBMSs) has attracted intensive interest from both …

Database management systems (DBMSs) are notoriously difficult to deploy and administer.
The goal of a self-driving DBMS is to remove these impediments by managing itself …