BatANN delivers near-linear throughput scaling for distributed disk-based approximate nearest neighbor search on a single global graph, with 3.5-5.59x gains over scatter-gather baselines on 1B-point datasets at 0.95 recall.
Starling: An i/o- efficient disk-resident graph index framework for high- dimensional vector similarity search on data segment
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2025 2representative citing papers
DGAI decouples vector storage from graph topology in on-disk ANN indexes and adds similarity-aware dynamic layout plus hierarchical PQ two-stage querying to achieve 8x faster insertions/deletions and 67% lower peak query latency under mixed workloads.
citing papers explorer
-
Passing the Baton: High Throughput Distributed Disk-Based Vector Search with BatANN
BatANN delivers near-linear throughput scaling for distributed disk-based approximate nearest neighbor search on a single global graph, with 3.5-5.59x gains over scatter-gather baselines on 1B-point datasets at 0.95 recall.
-
DGAI: Decoupled On-Disk Graph-Based ANN Index for Efficient Updates and Queries
DGAI decouples vector storage from graph topology in on-disk ANN indexes and adds similarity-aware dynamic layout plus hierarchical PQ two-stage querying to achieve 8x faster insertions/deletions and 67% lower peak query latency under mixed workloads.