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 …

Manually integrating data of diverse formats and languages is vital to many artificial
intelligence applications. However, the task itself remains challenging and time-consuming …

Existing machine learning (ML) approaches to automatically optimize database
management systems (DBMSs) only target a single configuration space at a time (eg, knobs …

Autonomous database management systems (DBMSs) aim to optimize themselves
automatically without human guidance. They rely on machine learning (ML) models that …

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

Data compression has recently experienced a revival in the domain of in-memory column
stores. In this field, a large corpus of lightweight integer compression algorithms plays a …

In this paper, we present\textsc {JoinGym}, an efficient and lightweight query optimization
environment for reinforcement learning (RL). Join order selection (JOS) is a classic NP-hard …

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

Join order selection (JOS), the ordering of join operations to minimize query execution cost,
is a core NP-hard combinatorial optimization problem in database query optimization. We …

With the development of cloud database, the simulation system of RDBMSs become
increasing important for avoiding database failures. For example, a simulation system could …