大型动态道路网络上的高吞吐量最短距离查询处理
High Throughput Shortest Distance Query Processing on Large Dynamic Road Networks
AI总结：
针对大型动态道路网络的高吞吐量最短路径查询，本文提出PSP、PMHL及PostMHL索引与跨边界优化，实验显示吞吐量最高提升2个数量级。
摘要：
最短路径（SP）计算是许多基于位置服务的构建模块，实现具有实时响应的高吞吐量SP查询处理对这些服务至关重要。然而，由于查询效率较低或动态适应性较差，现有解决方案几乎无法处理大型动态道路网络上的高吞吐量查询。本文利用图划分，并提出新颖的分区最短路径（PSP）索引来解决该问题。具体而言，我们首先提出一种跨边界策略以加速PSP索引的查询处理，并从理论上分析其效率上界。随后，我们提出一种非平凡的分区多阶段枢纽标注（PMHL），它巧妙地聚合多种PSP策略，以在索引更新期间实现快速索引维护和持续的查询效率提升。最后，为进一步优化吞吐量，我们设计了基于树分解的图划分，并提出具有更快查询处理和索引更新能力的后分区MHL（PostMHL）。在真实道路网络上的实验表明，我们的方法在查询吞吐量方面优于最先进的基线，提升幅度最高达2个数量级。
英文摘要：
Shortest path (SP) computation is the building block for many location-based services, and achieving high throughput SP query processing with real-time response is crucial for those services. However, existing solutions can hardly handle high throughput queries on large dynamic road networks due to either slow query efficiency or poor dynamic adaption. In this paper, we leverage graph partitioning and propose novel Partitioned Shortest Path (PSP) indexes to address this problem. Specifically, we first put forward a cross-boundary strategy to accelerate the query processing of PSP index and analyze its efficiency upper bound theoretically. After that, we propose a non-trivial Partitioned Multi-stage Hub Labeling (PMHL) that subtly aggregates multiple PSP strategies to achieve fast index maintenance and consecutive query efficiency improvement during index update. Lastly, to further optimize throughput, we design tree decomposition-based graph partitioning and propose Post-partitioned MHL (PostMHL) with faster query processing and index update. Experiments on real-world road networks show that our methods outperform state-of-the-art baselines in query throughput, yielding up to 2 orders of magnitude improvement.
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。
