The problem of searching the elements of a set that are close to a given query element
under some similarity criterion has a vast number of applications in many branches of …

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 …

We present a new approach for the approximate K-nearest neighbor search based on
navigable small world graphs with controllable hierarchy (Hierarchical NSW, HNSW). The …

Technology development has accelerated the volume growth of complex data, such as
images, videos, time series, and georeferenced data. Similarity search is a widely used …

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

We propose a novel approach to solving the approximate k-nearest neighbor search
problem in metric spaces. The search structure is based on a navigable small world graph …

The problem of inducing, learning or inferring grammars has been studied for decades, but
only in recent years has grammatical inference emerged as an independent field with …

Applications increasingly leverage mixed-modality data, and must jointly search over vector
data, such as embedded images, text and video, as well as structured data, such as …

Existing studies on time series are based on two categories of distance functions. The first
category consists of the Lp-norms. They are metric distance functions but cannot support …

The area of similarity searching is a very hot topic for both research and c-mercial
applications. Current data processing applications use data with c-siderably less structure …