Collaborative Research: ATD: a-DMIT: a novel Distributed, MultI-channel, Topology-aware online monitoring framework of massive spatiotemporal data

合作研究：ATD：a-DMIT：一种新颖的分布式、多通道、拓扑感知的海量时空数据在线监测框架

• 批准号: 2220495
• 负责人: Yao Xie
• 金额: $ 10万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2220495&HistoricalAwards=false
• 关键词: Collaborative; Research; ATD; DMIT; novel

Given current technological, societal and environmental changes, we face ever more diverse potentially destructive threats. Technology has enabled the collection of massive spatiotemporal datasets, which can be used for real-time identification of potential threats. The complexities of such data create exciting challenges for online threat detection, involving the learning and integration of complex nonlinear embeddings for efficient monitoring, the fusion of multiple spatiotemporal data sources for improving detection performance, and scalability for real-time implementation on distributed computing systems. This project will develop a novel Distributed, MultI-source, Topology-aware (a-DMIT) online threat detection framework that tackles these challenges for massive, high-dimensional spatiotemporal data. In developing reliable, scalable and versatile threat detection methods (supported by theory and algorithms), a-DMIT has the potential to improve national health and defense in a broad range of areas, including environmental monitoring, crime monitoring and mobile health. The a-DMIT project will contribute to education by involving undergraduate and graduate students in the research, and developed software will be made publicly available. a-DMIT will develop three new detection methods that jointly tackle fundamental challenges in online monitoring of massive data streams. The first method, called PERsistence diagram-based ChangE-PoinT detection (PERCEPT), is a novel non-parametric, topology-aware algorithm that extends state-of-the-art tools in topological data analysis for efficient monitoring of high-dimensional data streams. The second, called MUlti-source Monitoring via Gaussian Processes (MUM-GP), is an efficient online Bayesian non-parametric detection method for multi-source spatiotemporal data. The third, called Conditional Auto-Regressive Distributed (CARD) detection, is an online spatiotemporal network monitoring procedure that leverages neighboring spatial information in a distributed and decentralized fashion. a-DMIT will be usable for a wide range of modern threat detection applications, including environmental monitoring, crime monitoring, satellite image monitoring and power grid security.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

鉴于当前的技术、社会和环境变化，我们面临着更加多样化的潜在破坏性威胁。技术使收集大量时空数据集成为可能，这些数据集可用于实时识别潜在威胁。这些数据的复杂性为在线威胁检测带来了令人兴奋的挑战，包括学习和集成复杂的非线性嵌入以实现有效监测，融合多个时空数据源以提高检测性能，以及在分布式计算系统上实时实现的可扩展性。该项目将开发一种新颖的分布式、多源、拓扑感知（a- dmit）在线威胁检测框架，以应对大规模、高维时空数据的这些挑战。在开发可靠、可扩展和通用的威胁检测方法（由理论和算法支持）方面，a- dmit有潜力在广泛的领域改善国家卫生和国防，包括环境监测、犯罪监测和移动卫生。a-DMIT项目将通过让本科生和研究生参与研究，为教育做出贡献，开发的软件将向公众开放。a-DMIT将开发三种新的检测方法，共同解决在线监测海量数据流的基本挑战。第一种方法，称为基于持久性图的变更点检测（PERCEPT），是一种新颖的非参数拓扑感知算法，它扩展了拓扑数据分析中最先进的工具，用于有效监控高维数据流。第二种方法是基于高斯过程的多源监测（MUlti-source Monitoring via Gaussian Processes，简称num - gp），是一种针对多源时空数据的高效在线贝叶斯非参数检测方法。第三种，称为条件自回归分布式（CARD）检测，是一种在线时空网络监测程序，以分布式和分散的方式利用邻近的空间信息。a- dmit将可用于广泛的现代威胁检测应用，包括环境监测、犯罪监测、卫星图像监测和电网安全。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。