Vector data is prevalent across business and scientific applications, and its popularity is
growing with the proliferation of learned embeddings. Vector data collections often reach …

Approximate nearest neighbor search on high-dimensional vectors is a crucial component
for numerous applications in various fields. To solve this problem efficiently, dozens of …

Graph-based indexes have been widely employed to accelerate approximate similarity
search of high-dimensional vectors. However, the performance of graph indexes to answer …

Neural audio codec tokens serve as the fundamental building blocks for speech language
model (SLM)-based speech generation. However, there is no systematic understanding on …

Locality-sensitive hashing (LSH) is a well-known solution for approximate nearest neighbor
(ANN) search in high-dimensional spaces due to its robust theoretical guarantee on query …

Distance measures are core building blocks in time-series analysis and the subject of active
research for decades. Unfortunately, the most detailed experimental study in this area is …

By empowering domain experts to perform interactive exploration of large time series
datasets, sketch-based query interfaces have revitalized interest in the well-studied problem …

Approximate Nearest Neighbor (ANN) search in high-dimensional Euclidean spaces is a
fundamental problem with a wide range of applications. However, there is currently no ANN …

Efficiently finding similar items in large datasets is a crucial task with applications in various
domains. This abstract presents a survey of algorithms and techniques designed for this …