AI 中文总结
本文提出RaG-Tree，一种结合R树与感知划分HNSW图的统一MR-ANNS索引，通过分层R树剪枝、适配局部分布的HNSW图及自适应搜索算法，在三个真实数据集上实现更优查询性能与轻量级索引、快速增量更新。
AI 中文摘要
多属性范围过滤近似最近邻搜索（MR-ANNS）是现代AI应用中的基础操作，用于检索满足多个属性约束的高维向量。现有MR-ANNS索引要么利用单一属性进行范围定位，要么沿单个属性递归划分对象，这可能限制其利用属性相关性进行有效范围剪枝、利用属性-向量相关性进行高效近邻搜索的能力。本文提出RaG-Tree，一种将R树与感知划分的HNSW图耦合的统一MR-ANNS索引。RaG-Tree利用分层R树划分实现有效范围剪枝，并使每个HNSW图的稀疏度适配其划分内的局部向量分布，从而实现轻量级索引与高效查询处理。为支持高效查询处理与动态更新，我们开发了一种基于代价的自适应搜索算法，可最小化不必要的图遍历，同时设计了一种高效的索引维护机制，用于增量更新受影响的感知划分的HNSW图。在三个真实世界数据集上的大量实验表明，RaG-Tree相比现有最优基准方法实现了更优的查询性能，同时提供轻量级索引与快速增量更新能力。
英文摘要
Multi-attribute range-filtered approximate nearest neighbor search (MR-ANNS), which retrieves high-dimensional vectors satisfying multiple attribute constraints, is a fundamental operation in modern AI applications. Existing MR-ANNS indexes either exploit a single attribute for range localization or recursively partition objects along individual attributes, which may limit their ability to exploit attribute correlations for effective range pruning and attribute-vector correlations for efficient nearest-neighbor search. In this paper, we propose RaG-Tree, a unified index that couples an R-tree with partition-aware HNSW graphs for MR-ANNS. RaG-Tree leverages hierarchical R-tree partitions for effective range pruning and adapts the sparsity of each HNSW graph to the local vector distributions within its partition, enabling lightweight indexing and efficient query processing. To support efficient query processing and dynamic updates, we develop a cost-based adaptive search algorithm that minimizes unnecessary graph exploration, together with an efficient index maintenance mechanism for incrementally updating affected partition-aware HNSW graphs. Extensive experiments on three real-world datasets show that RaG-Tree achieves superior query performance over state-of-the-art baselines, while also providing lightweight indexing and fast incremental updates.
Comments13 pages
