DIDS — Double Indices and Double Summarizations for Fast Similarity Search

Paper proposing the Satellite System Graph (NSSG) approach for high dimensional similarity search, emphasizing efficiency, scalability, and unindexed query compatibility. Published in TPAMI 2021 by Fu et al.

IDEA is an inverted, deduplication-aware index structure designed to improve storage efficiency and query performance for similarity search workloads. It is implemented as research code and targets high-dimensional vector and content-addressable data, making it relevant to large-scale vector database and ANN indexing systems.

OasysDB is an open-source vector database focused on efficient similarity search and management of high-dimensional data.

Vexvault is an open-source vector database designed for efficient storage, management, and similarity search of high-dimensional vector data.

K-means Tree is a clustering-based data structure that organizes high-dimensional vectors for fast similarity search and retrieval. It is used as an indexing method in some vector databases to optimize performance for vector search operations.

Locality-Sensitive Hashing (LSH) is an algorithmic technique for approximate nearest neighbor search in high-dimensional vector spaces, commonly used in vector databases to speed up similarity search while reducing memory footprint.