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R-tree is a tree data structure widely used for indexing multi-dimensional information such as vectors, supporting efficient spatial queries like nearest neighbor and range queries, which are essential in vector databases.
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.
M-tree is a dynamic index structure for organizing and searching large data sets in metric spaces, enabling efficient nearest neighbor queries and dynamic updates, which are important features for vector databases handling high-dimensional vectors.
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.
Online Product Quantization (O-PQ) is a variant of product quantization designed to support dynamic or streaming data. It enables adaptive updating of quantization codebooks and codes in real-time, making it suitable for vector databases that handle evolving datasets.
Optimized Product Quantization (OPQ) enhances Product Quantization by optimizing space decomposition and codebooks, leading to lower quantization distortion and higher accuracy in vector search. OPQ is widely used in advanced vector databases for improving recall and search quality.
Product Quantization (PQ) is a technique for compressing high-dimensional vectors into compact codes, enabling efficient approximate nearest neighbor (ANN) search in vector databases. PQ reduces memory footprint and search time, making it a foundational algorithm for large-scale vector search systems.
Category: Concepts & Definitions
Tags: data-structure, spatial-indexing, vector-search, nearest-neighbor
R-tree is a balanced tree data structure designed for indexing multi-dimensional information such as geographical coordinates, rectangles, or polygons. It supports efficient spatial queries like nearest neighbor and range searches, making it widely used in spatial databases and vector search applications.