iRangeGraph: Improvising Range-dedicated Graphs for Range-filtering Nearest Neighbor Search
Yuexuan Xu, Jianyang Gao, Yutong Gou, Cheng Long, Christian S. Jensen

TL;DR
This paper introduces a novel approach for range-filtering approximate nearest neighbor search by using elemental graphs to efficiently construct query-specific indexes, improving performance and reducing memory use.
Contribution
It proposes a method to build elemental graphs for a limited set of ranges and efficiently construct indexes for arbitrary ranges during querying, enhancing performance over lossy compressed indexes.
Findings
Elemental graphs consume moderate space.
The method achieves superior query performance.
Stable performance across various workloads.
Abstract
Range-filtering approximate nearest neighbor (RFANN) search is attracting increasing attention in academia and industry. Given a set of data objects, each being a pair of a high-dimensional vector and a numeric value, an RFANN query with a vector and a numeric range as parameters returns the data object whose numeric value is in the query range and whose vector is nearest to the query vector. To process this query, a recent study proposes to build dedicated graph-based indexes for all possible query ranges to enable efficient processing on a database of objects. As storing all these indexes is prohibitively expensive, the study constructs compressed indexes instead, which reduces the memory consumption considerably. However, this incurs suboptimal performance because the compression is lossy. In this study, instead of materializing a compressed index for every possible…
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Taxonomy
TopicsData Management and Algorithms · Data Mining Algorithms and Applications · Geographic Information Systems Studies
MethodsSoftmax · Attention Is All You Need · Sparse Evolutionary Training
