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 …

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 …

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 …

The semantic capabilities of language models (LMs) have the potential to enable rich
analytics and reasoning over vast knowledge corpora. Unfortunately, existing systems lack …

Approximate nearest neighbor (ANN) search is a fundamental search in multi-dimensional
databases, which has numerous real-world applications, such as image retrieval …

Data series indexes are necessary for managing and analyzing the increasing amounts of
data series collections that are nowadays available. These indexes support both exact and …

Maximum inner product search (MIPS) in high-dimensional spaces has wide applications
but is computationally expensive due to the curse of dimensionality. Existing studies employ …

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 …

Large, sparse categorical data is a natural way to represent complex data like sequences,
trees, and graphs. Such data is prevalent in many applications, eg, Criteo released a …

Owing to the widespread deployment of smartphones and networked devices, massive
amount of data in different types are generated every day, including numeric data, locations …