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 …

In recent years, we have seen increased interest in applying machine learning to system
problems. For example, there has been work on applying machine learning to improve …

Enterprises use distributed database systems to meet the demands of mixed or hybrid
transaction/analytical processing (HTAP) workloads that contain both transactional (OLTP) …

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 …

There is an increasing adoption of machine learning for encoding data into vectors to serve
online recommendation and search use cases. As a result, recent data management …

Spatial data is ubiquitous. Massive amounts of data are generated every day from a plethora
of sources such as billions of GPS-enabled devices (eg, cell phones, cars, and sensors) …

The emerging class of instance-optimized systems has shown potential to achieve high
performance by specializing to a specific data and query workloads. Particularly, Machine …

Machine learning techniques have been proposed to optimize data management in recent
years. Compared with traditional empirical data management, learning-based methods …

The exponential growth of spatial data poses new challenges to the performance of spatial
databases. Spatial indexes like R-tree greatly accelerate the query performance and can be …

The surge in data analytics has fostered burgeoning demand for AnalyticDB on Alibaba
Cloud, which has well served thousands of customers from various business sectors. The …