Finding nearest neighbors in high-dimensional spaces is a fundamental operation in many
diverse application domains. Locality Sensitive Hashing (LSH) is one of the most popular …

Increasingly large data series collections are becoming commonplace across many different
domains and applications. A key operation in the analysis of data series collections is …

Nearest neighbor search is a fundamental and essential operation in applications from
many domains, such as databases, machine learning, multimedia, and computer vision …

Locality-Sensitive Hashing (LSH) and its variants are the well-known indexing schemes for
the c-Approximate Nearest Neighbor (c-ANN) search problem in high-dimensional …

Nearest neighbor (NN) search in high-dimensional spaces is inherently computationally
expensive due to the curse of dimensionality. As a well-known solution to approximate NN …

We propose CXL-ANNS, a software-hardware collaborative approach to enable highly
scalable approximate nearest neighbor search (ANNS) services. To this end, we first …

Space partitions of $\mathbb {R}^ d $ underlie a vast and important class of fast nearest
neighbor search (NNS) algorithms. Inspired by recent theoretical work on NNS for general …

Data series are a special type of multidimensional data present in numerous domains,
where similarity search is a key operation that has been extensively studied in the data …

Vector search has emerged as the foundation for large-scale information retrieval and
machine learning systems, with search engines like Google and Bing processing tens of …

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 …