UNIFY：面向范围过滤近似最近邻搜索的统一索引
UNIFY: Unified Index for Range Filtered Approximate Nearest Neighbors Search
AI总结：
针对现有范围过滤近似最近邻搜索方法在查询范围变化时性能下降、多专用索引存在一致性维护问题，本文提出UNIFY框架，基于新型分段包含图及其分层变体，统一支持三类过滤策略，在不同查询范围下均实现最优性能。
摘要：
本文提出了一种高效可扩展的框架，用于处理带属性值的高维向量上的范围过滤近似最近邻搜索（RF-ANNS）。给定查询向量$q$和范围$[l, h]$，RF-ANNS的目标是在属性值落在$[l, h]$范围内的数据中，找到$q$的近似$k$个最近邻。现有方法包括在ANNS过程之前、之后或期间执行属性范围过滤的预过滤、后过滤和混合过滤策略，当查询范围发生变化时，这些方法的性能都会出现显著下降。虽然为每种策略构建专用索引并根据查询范围选择最优方案可以解决这一问题，但会引发索引一致性和维护问题。\n我们的框架名为UNIFY，它构建了一个统一的基于近邻图（PG-based）的索引，可无缝支持全部三种策略。在UNIFY中，我们提出了一种新型的分段包含图（Segmented Inclusive Graph，SIG），它按属性值对数据集进行分段。SIG确保任意分段组合中对象的近邻图（PG）都是SIG的子图，从而能够通过从相关分段重建并搜索PG来实现高效的混合过滤。此外，我们还提出了分层分段包含图（Hierarchical Segmented Inclusive Graph，HSIG），它是SIG的一个变体，借鉴HNSW的分层结构，实现了对数级的混合过滤复杂度。我们还通过将跳表连接和压缩的HNSW边融合到分层图中，为HSIG实现了预过滤和后过滤功能。实验结果表明，在小、中、大三种查询范围下，UNIFY都能达到当前最优的RF-ANNS性能。
英文摘要：
This paper presents an efficient and scalable framework for Range Filtered Approximate Nearest Neighbors Search (RF-ANNS) over high-dimensional vectors associated with attribute values. Given a query vector $q$ and a range $[l, h]$, RF-ANNS aims to find the approximate $k$ nearest neighbors of $q$ among data whose attribute values fall within $[l, h]$. Existing methods including pre-, post-, and hybrid filtering strategies that perform attribute range filtering before, after, or during the ANNS process, all suffer from significant performance degradation when query ranges shift. Though building dedicated indexes for each strategy and selecting the best one based on the query range can address this problem, it leads to index consistency and maintenance issues. Our framework, called UNIFY, constructs a unified Proximity Graph-based (PG-based) index that seamlessly supports all three strategies. In UNIFY, we introduce SIG, a novel Segmented Inclusive Graph, which segments the dataset by attribute values. It ensures the PG of objects from any segment combinations is a sub-graph of SIG, thereby enabling efficient hybrid filtering by reconstructing and searching a PG from relevant segments. Moreover, we present Hierarchical Segmented Inclusive Graph (HSIG), a variant of SIG which incorporates a hierarchical structure inspired by HNSW to achieve logarithmic hybrid filtering complexity. We also implement pre- and post-filtering for HSIG by fusing skip list connections and compressed HNSW edges into the hierarchical graph. Experimental results show that UNIFY delivers state-of-the-art RF-ANNS performance across small, mid, and large query ranges.
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。
