Recently, there has been a pressing need to manage high-dimensional vector data in data
science and AI applications. This trend is fueled by the proliferation of unstructured data and …

The KNN algorithm is one of the most popular data mining algorithms. It has been widely
and successfully applied to data analysis applications across a variety of research topics in …

The approximate nearest neighbor (ANN) search in high-dimensional spaces is a
fundamental but computationally very expensive problem. Many methods have been …

With the development of learning-based embedding models, embedding vectors are widely
used for analyzing and searching unstructured data. As vector collections exceed billion …

Approximate nearest neighbor search is an important research topic with a wide range of
applications. In this study, we first introduce the problem and review major research results …

Approximate K nearest neighbor (AKNN) search in the high-dimensional Euclidean vector
space is a fundamental and challenging problem. We observe that in high-dimensional …

Locality-sensitive hashing (LSH) is a promising family of methods for the high-dimensional
approximate nearest neighbor (ANN) search problem due to its sub-linear query time and …

Approximate nearest neighbor search (ANNS) is a fundamental problem that has a wide
range of applications in information retrieval and data mining. Among state-of-the-art in …

High-dimensional vector similarity search (HVSS) is gaining prominence as a powerful tool
for various data science and AI applications. As vector data scales up, in-memory indexes …

We investigate the problem of multimodal search of target modality, where the task involves
enhancing a query in a specific target modality by integrating information from auxiliary …