UNIFY: Unified Index For Range Filtered Approximate Nearest Neighbors Search
2024 · Anqi Liang, Pengcheng Zhang, Bin Yao, et al.
Abstract
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
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