iRangeGraph：面向范围过滤最近邻搜索的即兴范围专用图
iRangeGraph: Improvising Range-dedicated Graphs for Range-filtering Nearest Neighbor Search
AI总结：
iRangeGraph通过为中等数量范围物化元素图并在查询时即时组合构建任意范围索引，解决了RFANN搜索中全量范围索引存储代价过高与压缩索引性能损失的问题，在真实数据集上以适度空间实现了优越且稳定的查询性能。
摘要：
范围过滤近似最近邻（RFANN）搜索在学术界和工业界正受到越来越多的关注。给定一组数据对象，每个对象由一个高维向量和一个数值组成，一个以向量和数值范围为参数的RFANN查询会返回数值落在查询范围内且向量与查询向量最接近的数据对象。为了处理这种查询，最近的一项研究提出为所有可能的查询范围构建O(n^2)个专用图索引，以便在包含n个对象的数据库上实现高效处理。由于存储所有这些索引的代价极其高昂，该研究转而构建压缩索引，从而大幅降低了内存消耗。然而，由于压缩是有损的，这导致了次优的性能。在本研究中，我们不再为每个可能的查询范围预先物化压缩索引，而是为中等数量的范围物化基于图的索引，称为元素图（elemental graphs）。然后，我们提供了一种有效且高效的算法，在查询过程中可以利用元素图为任意查询范围构建索引。我们证明了构建这种索引所需的时间很低。我们还涵盖了一项在真实世界数据集上的实验研究，该研究提供了证据表明，物化的元素图只消耗适度的空间，并且所提出的方法能够在不同的查询工作负载下实现优越且稳定的查询性能。
英文摘要：
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 $O(n^2)$ dedicated graph-based indexes for all possible query ranges to enable efficient processing on a database of $n$ 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 query range in preparation for querying, we materialize graph-based indexes, called elemental graphs, for a moderate number of ranges. We then provide an effective and efficient algorithm that during querying can construct an index for any query range using the elemental graphs. We prove that the time needed to construct such an index is low. We also cover an experimental study on real-world datasets that provides evidence that the materialized elemental graphs only consume moderate space and that the proposed method is capable of superior and stable query performance across different query workloads.
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。
