Proposes Unified Dominance Graph (UDG) for interval-predicate ANNS by mapping to dominance space and building a predicate-specific graph index with patch edges for better search under filters.
Navigating labels and vectors: A unified approach to filtered approximate nearest neighbor search,
5 Pith papers cite this work, alongside 18 external citations. Polarity classification is still indexing.
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A machine-learned router predicts per-query recall for filtered ANN methods and selects the recall-QPS optimal one, outperforming fixed baselines on five unseen datasets.
PipeANN-Filter improves filtered vector search latency and throughput on SSD by exploring a superset of valid vectors identified via probabilistic filters and verifying attributes only after selecting top-k candidates.
FAVOR achieves 1.3-5x higher QPS at 95% Recall@10 for arbitrary filtered ANNS by combining exclusion-distance reshaping in HNSW graphs with a selectivity-driven router that switches between brute-force and optimized search.
Formalizes fine-grained access control for vector databases, compares enforcement strategies, and identifies open challenges in balancing policy compliance with search performance.
citing papers explorer
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Unified Dominance Graph for Interval-Predicate Approximate Nearest Neighbor Search
Proposes Unified Dominance Graph (UDG) for interval-predicate ANNS by mapping to dominance space and building a predicate-specific graph index with patch edges for better search under filters.
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Query-aware Routing for Filtered Approximate Nearest Neighbors Search
A machine-learned router predicts per-query recall for filtered ANN methods and selects the recall-QPS optimal one, outperforming fixed baselines on five unseen datasets.
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PipeANN-Filter: An Efficient Filtered Vector Search System on SSD
PipeANN-Filter improves filtered vector search latency and throughput on SSD by exploring a superset of valid vectors identified via probabilistic filters and verifying attributes only after selecting top-k candidates.
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FAVOR: Efficient Filter-Agnostic Vector ANNS Based on Selectivity-Aware Exclusion Distances
FAVOR achieves 1.3-5x higher QPS at 95% Recall@10 for arbitrary filtered ANNS by combining exclusion-distance reshaping in HNSW graphs with a selectivity-driven router that switches between brute-force and optimized search.
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Policy-aware Vector Search: A Vision for Fine Grained Access Control in Vector Databases
Formalizes fine-grained access control for vector databases, compares enforcement strategies, and identifies open challenges in balancing policy compliance with search performance.