AI 中文总结
RECAST是一种区域范围自适应索引，通过维护查询区域复用已计算距离，在多数据集多工作负载下，相比AV-tree等基线显著降低精确相似度搜索的距离计算量与查询时间。
AI 中文摘要
度量空间中的相似度搜索广泛应用于生物信息学、数据挖掘和推荐系统。精确相似度搜索主要依赖距离计算，而实际查询流往往集中在特定区域，而非均匀分布在整个空间。预构建索引在查询流之前构建，当查询集中在服务不足的区域时无法自适应。AV-tree等自适应索引在回答查询时通过计算距离构建索引，但会丢弃大量此类距离，且无法有效组织保留的距离以供复用。我们提出RECAST，一种用于精确相似度搜索的区域范围自适应索引。RECAST维护查询区域，在每个区域内积累回答早期查询时已计算的距离（已付费距离）以进行精确剪枝，并利用其剪枝效果的变化来推断传入查询是否仍集中在当前区域。当查询发生转移时，RECAST递归地将查询工作分派给子区域，因此仅在已付费距离仍然有效的地方积累和复用这些距离。在四个工作负载模式下的五个真实世界数据集上，RECAST的累积成本始终低于自适应基线和大多数预构建基线，与最先进的自适应基线AV-tree相比，每个查询的距离计算最多减少64%，查询时间最多减少46%。
英文摘要
Similarity search in metric spaces is widely used in bioinformatics, data mining, and recommender systems. Exact similarity search is dominated by distance computations, while real query streams often concentrate in specific regions rather than spreading uniformly across the space. Pre-built indexes are constructed before the query stream and cannot adapt when queries concentrate in poorly served regions. Adaptive indexes such as AV-tree build the index from distances computed while answering queries, but discard many of those distances and do not organize the retained distances effectively for reuse. We propose RECAST, a region-scoped adaptive index for exact similarity search. RECAST maintains query regions, accumulates distances already computed while answering earlier queries (paid distances) within each region for exact pruning, and uses changes in their pruning effect to infer whether incoming queries remain concentrated in the current region. When queries shift, RECAST recursively dispatches query work to child regions, so paid distances are accumulated and reused only where they remain effective. On five real-world datasets under four workload patterns, RECAST achieves consistently lower cumulative cost than the adaptive baseline and most pre-built baselines, reducing per-query distance computations by up to 64% and query time by up to 46% compared with the state-of-the-art adaptive baseline AV-tree.
