灾害响应社会感知中的数据外推

Siyu Gu, Chenji Pan, Hengchang Liu, Shen Li, Shaohan Hu, Lu Su, Shiguang Wang, Dong Wang, Md. Tanvir Al Amin, R. Govindan, C. Aggarwal, R. Ganti, M. Srivatsa, A. Bar-Noy, Peter Terlecky, T. Abdelzaher
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引用次数: 25

摘要

本文通过开发“填补”缺失部分的推理结果来增加传感数据的方法,补充了大量的社会传感文献。与趋势外推方法不同,我们专注于破坏性趋势变化发生的灾难场景的预测。比较了一组预测启发式(和标准趋势外推算法),它们主要使用空间相关性或主要使用时间相关性进行数据外推。评估结果表明,他们没有一个人一直做得很好。这是因为在灾难发生后,监测到的系统状态在相对平静的时期和破坏性变化的时期(如余震)之间交替。因此,一个好的预测算法需要将平稳期的基于时间的数据外推和变化期的空间数据外推智能地结合起来。本文开发了这样一个算法。该算法使用2012年11月飓风桑迪过后纽约市危机期间收集的数据进行测试。结果表明,可以实现一致的良好预测。这项工作在解决复杂系统受应力损伤传播的双峰性质方面是独一无二的,并提供了一个简单的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data Extrapolation in Social Sensing for Disaster Response
This paper complements the large body of social sensing literature by developing means for augmenting sensing data with inference results that "fill-in" missing pieces. Unlike trend-extrapolation methods, we focus on prediction in disaster scenarios where disruptive trend changes occur. A set of prediction heuristics (and a standard trend extrapolation algorithm) are compared that use either predominantly-spatial or predominantly-temporal correlations for data extrapolation purposes. The evaluation shows that none of them do well consistently. This is because monitored system state, in the aftermath of disasters, alternates between periods of relative calm and periods of disruptive change (e.g., aftershocks). A good prediction algorithm, therefore, needs to intelligently combine time-based data extrapolation during periods of calm, and spatial data extrapolation during periods of change. The paper develops such an algorithm. The algorithm is tested using data collected during the New York City crisis in the aftermath of Hurricane Sandy in November 2012. Results show that consistently good predictions are achieved. The work is unique in addressing the bi-modal nature of damage propagation in complex systems subjected to stress, and offers a simple solution to the problem.
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