With the increase in the number of Internet of Things (IoT) applications, the reliance on
robust networking, reliable communications, and efficient and secure data storage is …

Approximate nearest neighbor search (ANNS) in high-dimensional vector spaces has
become increasingly crucial in database and machine learning applications. Most previous …

The explosive growth of massive high-dimensional data requires capabilities for data
processing, storing, and analyzing. This brings significant challenges to traditional …

Embedding retrieval is a crucial task for recommendation systems. Graph-based
approximate nearest neighbor search (GANNS) is the most commonly used method for …

Approximate nearest neighbor search (ANNS) is a key retrieval technique for vector
database and many data center applications, such as person re-identification and …

Graph processing plays a pivotal role in numerous large-scale applications, including social
and transportation networks. One of the primary challenges in handling large-scale graph …

Approximate nearest neighbor search (ANNS) is a key retrieval technique for vector
database and many data center applications, such as person re-identification and …

The ability to perform machine learning (ML) tasks in a database management system
(DBMS) is a new paradigm for conventional database systems as it enables advanced data …

Vector search systems are indispensable in large language model (LLM) serving, search
engines, and recommender systems, where minimizing online search latency is essential …

Graph is widely utilized as a key data structure in many applications, such as social network
and recommendation systems. However, many real-world graphs are constructed with large …