Zeng, Xianzhi, et al. "CANDY: A Benchmark for Continuous Approximate Nearest Neighbor Search with Dynamic Data Ingestion."

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.

Vector Index Benchmark for Embeddings - an extensible benchmarking suite for approximate nearest neighbor search methods using modern embedding datasets. VIBE addresses limitations of traditional ANN benchmarks by focusing on contemporary embedding models and datasets.

A comprehensive benchmarking project that evaluates and compares implementations of approximate nearest neighbor algorithms. Provides standardized datasets and metrics for comparing ANN libraries including FAISS, HNSW, Annoy, and ScaNN.

Main competition for large-scale vector database algorithms held at NeurIPS conferences. Framework for evaluating approximate nearest neighbor search algorithms on billion-scale datasets with standardized metrics and datasets.

Billion-scale approximate nearest neighbor search benchmark competition. Features datasets like SIFT1B, Deep1B with standardized evaluation metrics for comparing vector search algorithms at scale.

Billion-scale benchmark dataset containing 128-dimensional SIFT descriptors of one billion images. Widely used standard for evaluating approximate nearest neighbor search algorithms at scale.