Distance Comparison Operators for Approximate Nearest Neighbor Search: Exploration and Benchmark

PANTHER provides private ANN search in single server settings. Relevant for secure vector databases in AI. Cryptology ePrint Archive (2024) by Jingyu Li et al.

Distributed disk-based approximate nearest neighbor system achieving near-linear throughput scaling. Delivers 6.21-6.49x throughput improvement over scatter-gather baseline with sub-6ms latency on 10 servers.

Manifold-Consistent Graph Indexing for billion-scale disk-resident vector search. Leverages Local Intrinsic Dimensionality to achieve 5.8x throughput improvement over DiskANN on high-dimensional datasets.

Incremental in-place update system for billion-scale vector search from Microsoft Research. Maintains 2.41x lower P99.9 latency than baselines while supporting efficient vector updates with minimal resource overhead.

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.

Graph-based similarity search algorithm achieving 0.99 recall, building indexes 3-8x faster than competitors with 40% less memory. Answers 1-NN queries up to 10x faster than serial scan.