多属性范围过滤下的高效近似最近邻搜索
Efficient Approximate Nearest Neighbor Search under Multi-Attribute Range Filter
AI总结：
本文提出KHI索引，通过结合属性空间划分树与HNSW图，实现多属性范围过滤下的高效近似最近邻搜索，显著提升查询吞吐量和召回率。
摘要：
在现代人工智能和数据库系统中，高维向量的最近邻搜索是基础性技术。在许多实际应用中，查询涉及对多个数值属性的约束，从而产生范围过滤的近似最近邻搜索（RFANNS）。虽然已存在针对单属性范围谓词的RFANNS索引，但将其扩展到多属性场景是非平凡的，且往往效果不佳。在本文中，我们提出KHI，一种用于多属性RFANNS的索引，它结合了属性空间划分树与附加在树节点上的HNSW图。一个考虑偏斜的分裂规则通过$O(\log n)$限制树的高度，并通过树路由和在HNSW图上运行贪心搜索来回答查询。在四个真实世界数据集上的实验表明，KHI在保持高召回率的同时，能够实现高查询吞吐量。与最先进的RFANNS基线相比，KHI在平均情况下将QPS提高了2.46倍，在困难数据集上最高提高了16.22倍，对于较小的选择性、较大的k值和较高的谓词基数，增益更大。
英文摘要：
Nearest neighbor search on high-dimensional vectors is fundamental in modern AI and database systems. In many real-world applications, queries involve constraints on multiple numeric attributes, giving rise to range-filtering approximate nearest neighbor search (RFANNS). While there exist RFANNS indexes for single-attribute range predicates, extending them to the multi-attribute setting is nontrivial and often ineffective. In this paper, we propose KHI, an index for multi-attribute RFANNS that combines an attribute-space partitioning tree with HNSW graphs attached to tree nodes. A skew-aware splitting rule bounds the tree height by $O(\log n)$, and queries are answered by routing through the tree and running greedy search on the HNSW graphs. Experiments on four real-world datasets show that KHI consistently achieves high query throughput while maintaining high recall. Compared with the state-of-the-art RFANNS baseline, KHI improves QPS by $2.46\times$ on average and up to $16.22\times$ on the hard dataset, with larger gains for smaller selectivity, larger $k$, and higher predicate cardinality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
