论文标题
基于时空轨迹的有效的可疑感染人群检测
Efficient Suspected Infected Crowds Detection Based on Spatio-Temporal Trajectories
论文作者
论文摘要
从人到人的病毒传播是全球公众面临的紧急事件。早期发现和隔离潜在易感人群可以有效控制其疾病的流行。现有指标无法正确解决轨迹上的感染率。为了解决这个问题，我们提出了一种基于人类移动轨迹的新型时空感染率（IR）度量，可以充分描述被患者的特定查询轨迹感染的风险。然后，我们通过有效的时空指数来管理源数据，以使我们的系统更可扩展，并可以快速从大型轨迹中查询易感人群。此外，我们设计了几种可以有效减少计算的修剪策略。此外，我们设计了第一次空间（SFT）索引，这使我们能够快速查询多个轨迹，而无需大量的I/O消耗和数据冗余。基于实际和合成轨迹数据集的实验中证明了溶液的性能，这些数据集已显示我们的解决方案的有效性和效率。
Virus transmission from person to person is an emergency event facing the global public. Early detection and isolation of potentially susceptible crowds can effectively control the epidemic of its disease. Existing metrics can not correctly address the infected rate on trajectories. To solve this problem, we propose a novel spatio-temporal infected rate (IR) measure based on human moving trajectories that can adequately describe the risk of being infected by a given query trajectory of a patient. Then, we manage source data through an efficient spatio-temporal index to make our system more scalable, and can quickly query susceptible crowds from massive trajectories. Besides, we design several pruning strategies that can effectively reduce calculations. Further, we design a spatial first time (SFT) index, which enables us to quickly query multiple trajectories without much I/O consumption and data redundancy. The performance of the solutions is demonstrated in experiments based on real and synthetic trajectory datasets that have shown the effectiveness and efficiency of our solutions.
