Learned databases, or AI4DB techniques, have rapidly developed in the last decade.
Deploying machine learning (ML) and AI4DB algorithms into actual databases is the gold …

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

Numerous applications today rely on artificial intelligence over images. Image AI is,
however, extremely expensive. In particular, the inference cost of image AI dominates the …

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

We use machine learning to optimize LSM-tree structure, aiming to reduce the cost of
processing various read/write operations. We introduce a new approach CAMAL, which …

Learned Index Structures (LIS) view a sorted index as a model that learns the data
distribution, takes a data element key as input, and outputs the predicted position of the key …

The process of database knob tuning has always been a challenging task. Recently,
database knob tuning methods has emerged as a promising solution to mitigate these …

We propose CASA, a classification-based admission control strategy for use in distributed
query processing systems. CASA uses coarse-grained predictions of CPU and memory …

Since Gartner coined the term, Hybrid Transactional and Analytical Processing (HTAP),
numerous HTAP databases have been proposed to combine transactions with analytics in …

The ever-increasing improvement in computer storage and network performance means that
disk I/O and network communication are often no longer bottlenecks in database …