A C++ library for approximate nearest neighbor searching in arbitrarily high dimensions, developed by David Mount and Sunil Arya at the University of Maryland. Provides data structures and algorithms for both exact and approximate nearest neighbor searching.

Research paper by Chen et al. examining the meaningfulness of nearest neighbor search in high-dimensional spaces. Analyzes limitations and implications for vector similarity search. Key for understanding ANN effectiveness.

MRPT (Multi-Resolution Proximity Trees) is an open-source library for fast approximate nearest neighbor search in high-dimensional vector spaces, applicable to vector database backends.

Framework by Wei et al. for high-dimensional ANN search using subspace collision techniques. Offers efficiency and accuracy improvements for vector databases.

VLDB 2024 paper presenting DIDS, a fast similarity search method using double indices and double summarizations to accelerate high-dimensional vector queries.

Research paper on high-dimensional approximate nearest neighbor search focusing on reliable and efficient distance comparison operations. Published in Proceedings of the ACM on Management of Data, Volume 1, Issue 2 in 2023 by Jianyang Gao and Cheng Long.