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arXiv 2602.05928cs.DB
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更快的地理社交可达性查询

Even Faster Geosocial Reachability Queries

Eindhoven University of Technology(埃因霍温理工大学)

Rick van der Heijden, Nikolay Yakovets, Thekla Hamm

AI总结:

2DReach通过2D R树替代3DReach的区间标记,实现更快的索引构建和更稳定的查询性能。

摘要:

地理社交可达性查询(RangeReach)确定给定顶点是否能在地理社交网络中到达查询区域内的任何空间顶点。最先进的3DReach方法通过区间标记和在3D R树中索引空间顶点来编码图的可达性。我们提出了2DReach,一种更简单的方法,完全避免了区间标记。与3DReach类似,2DReach将强连通分量(SCCs)折叠成DAG,但不像3DReach计算区间标签,而是直接为每个组件存储一个2D R树,涵盖所有可达的空间顶点。查询则减少为单个2D R树查找。我们进一步提出了压缩变体,通过排除空间汇点和在具有相同可达集的组件之间共享R树来减少存储。在四个真实世界数据集上的实验表明,2DReach的索引构建速度比3DReach更快,压缩变体在所有方法中具有最小的索引大小。2DReach在各种查询参数下提供竞争或更优的查询性能,响应时间更稳定。

英文摘要:

Geosocial reachability queries (\textsc{RangeReach}) determine whether a given vertex in a geosocial network can reach any spatial vertex within a query region. The state-of-the-art 3DReach method answers such queries by encoding graph reachability through interval labelling and indexing spatial vertices in a 3D R-tree. We present 2DReach, a simpler approach that avoids interval labelling entirely. Like 3DReach, 2DReach collapses strongly connected components (SCCs) into a DAG, but instead of computing interval labels, it directly stores a 2D R-tree per component over all reachable spatial vertices. A query then reduces to a single 2D R-tree lookup. We further propose compressed variants that reduce storage by excluding spatial sinks and sharing R-trees between components with identical reachable sets. Experiments on four real-world datasets show that 2DReach achieves faster index construction than 3DReach, with the compressed variant yielding the smallest index size among all methods. 2DReach delivers competitive or superior query performance with more stable response times across varying query parameters.

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。

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