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 …

There are now over 20 commercial vector database management systems (VDBMSs), all
produced within the past five years. But embedding-based retrieval has been studied for …

In the last two decades, interactive visualization and analysis have become a central tool in
data-driven decision making. Concurrently to the contributions in data visualization …

With the explosive growth of unstructured data (such as images, videos, and audios),
unstructured data analytics is widespread in a rich vein of real-world applications. Many …

Machine learning (ML) has widespread applications and has revolutionized many
industries, but suffers from several challenges. First, sufficient high-quality training data is …

The lack of sufficient labeled data is a key bottleneck for practitioners in many real-world
supervised machine learning (ML) tasks. In this paper, we study a new problem, namely …

Memory disaggregation (MD) allows for scalable and elastic data center design by
separating compute (CPU) from memory. With MD, compute and memory are no longer …

Cloud databases have been widely accepted and deployed due to their unique advantages,
such as high elasticity, high availability, and low cost. Many new techniques, such as …

Disaggregated database systems achieve unprecedented excellence in elasticity and
resource utilization at the cloud scale and have gained great momentum from both industry …

R-tree is a popular index which supports efficient queries on multi-dimensional data. The
performance of R-tree mostly depends on how the tree structure is built if new data instances …