ATD: Collaborative Research: Multivariate Quantiles for Rapid Spatio-Temporal Threat Detection

ATD：协作研究：用于快速时空威胁检测的多元分位数

• 批准号: 1737915
• 负责人: Ujjal Mukherjee
• 金额: $ 2.5万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1737915&HistoricalAwards=false
• 关键词: ATD; Collaborative; Research; Multivariate; Quantiles

Different kinds of data on societal attributes, observed from multiple sources, at multiple locations, and at different points in time, will be studied in this project. The geometrical properties of such data will be analyzed to quantify and characterize normal patterns in the data, which will then be leveraged to identify sudden departures from normal patterns within societies. Methodology for understanding normal patterns in the data and rapidly detecting change in one or more aspects of the data will be devised in this project. Data from different locations around the world will be analyzed and used to formulate strategies for risk mitigation and emergency responses.The geometric properties of high-dimensional spatio-temporal data will be studied in this project to construct a multi-dimensional extremity indicator. This indicator and other statistical and machine learning techniques will be used for rapid spatio-temporal change detection, under a variety of technical conditions and frameworks. Such changes may be towards specific known directions, or generic departures from normal patterns. Methods for detecting changes in extremes and tails of multivariate probability distributions will likewise be developed as part of this project. Social, economic, and supply chain logistics data will then be studied to develop policy and rapid response strategies using data-driven techniques.

本项目将研究从多个来源、多个地点和不同时间点观察到的关于社会属性的各种数据。将分析这些数据的几何特性，以量化和表征数据中的正常模式，然后利用这些模式来确定社会中突然偏离正常模式的情况。本项目将设计用于理解数据中的正常模式和快速检测数据的一个或多个方面的变化的方法。本项目将分析来自世界各地的数据，并用于制定风险缓解和应急响应策略。本项目将研究高维时空数据的几何特性，以构建多维极端指标。该指标和其他统计和机器学习技术将用于在各种技术条件和框架下快速检测时空变化。这种变化可能是朝向特定的已知方向，或者是从正常模式的一般偏离。作为本项目的一部分，还将开发检测多元概率分布极值和尾部变化的方法。然后将研究社会，经济和供应链物流数据，以使用数据驱动技术制定政策和快速反应战略。