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.
Private ANN in single-server model.
Free.
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Distance Comparison Operators for Approximate Nearest Neighbor Search: Exploration and Benchmark
Explores and benchmarks distance comparison operators for ANN. arXiv preprint arXiv:2403.13491 (2024) by Zeyu Wang et al. Aids in vector search optimization.
BatANN
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.
MCGI
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.
SPFresh
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.
ELPIS
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.
BANG
BANG is a billion-scale approximate nearest neighbor search system optimized for single GPU execution, enabling high-performance vector search in vector database environments at massive scale.
