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A unified system designed for efficient multi-index vector search, directly addressing large-scale vector database performance and scalability challenges.
A scalable system for approximate nearest neighbor search at web-scale, relevant for implementing and understanding vector database infrastructure for high-dimensional data.
This paper introduces the HNSW algorithm, which is widely adopted in vector databases and search engines for its efficient and robust performance on high-dimensional data. HNSW is foundational in powering modern vector search systems.
An influential paper analyzing and improving approximate nearest neighbor search methods for high-dimensional data, highly relevant for developing and understanding vector databases.
BANG is a billion-scale approximate nearest neighbor search system optimized for single GPU execution, enabling high-performance vector search in vector database environments at massive scale.
Ball-tree is a binary tree data structure used for organizing points in a multi-dimensional space, particularly useful in vector databases for nearest neighbor search. It partitions data points into hyperspheres (balls), enabling efficient search and scalability in high-dimensional vector spaces.
Category: Research Papers & Surveys
Tags: vector-search, performance, scalability, research
OneSparse is a unified system developed for efficient multi-index vector search, directly addressing performance and scalability challenges in large-scale vector databases. It is particularly relevant for applications such as recommendation systems and search engines that require efficient retrieval from hybrid data sets containing both sparse and dense vectors.
Not applicable (research paper).