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。技术使大量时空数据集的收集可以用于实时识别潜在威胁。此类数据的复杂性为在线威胁检测带来了令人兴奋的挑战，涉及复杂的非线性嵌入以进行有效监视的学习和集成，融合了多个时空数据源以改善检测性能以及在分布式计算系统上实时实现的可扩展性。该项目将开发出一种新颖的分布式，多源，拓扑感知（A-DMIT）在线威胁检测框架，该框架解决了这些挑战，以实现大规模的高维时空数据。在开发可靠，可扩展和多功能的威胁检测方法（由理论和算法支持）时，A-DMIT有可能在广泛的领域中改善国家健康和国防，包括环境监测，犯罪监测和移动健康。 A-DMIT项目将通过涉及本科生和研究生参与研究来为教育做出贡献，并且将公开提供开发的软件。 A-DMIT将开发三种新的检测方法，这些方法在在线监视大规模数据流中共同应对基本挑战。第一种方法，称为基于持久图的更改点检测（感知），是一种新型的非参数，拓扑感知算法，在拓扑数据分析中扩展了最新工具，以有效地监视高维数据流。第二个通过高斯工艺（MUM-GP）称为多源监测，是用于多源时空数据的有效在线贝叶斯非参数检测方法。第三个称为条件自动回归分布式（卡）检测是在线时空网络监控程序，以分布式和分散的方式利用相邻的空间信息。 A-DMIT可用于广泛的现代威胁检测应用程序，包括环境监测，犯罪监测，卫星图像监控和电网安全性。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来进行评估的。