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

鉴于当前技术、社会和环境的变化，我们面临着更加多样的潜在破坏性威胁。技术使收集海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。这些数据的复杂性给在线威胁检测带来了令人兴奋的挑战，涉及到学习和集成复杂的非线性嵌入以实现有效的监控，融合多个时空数据源以提高检测性能，以及可伸缩地在分布式计算系统上实时实施。这个项目将开发一个新的分布式、多源、拓扑感知(a-dmit)在线威胁检测框架，以应对这些针对海量、高维时空数据的挑战。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，ADMIT具有在广泛的领域改善国民健康和国防的潜力，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来促进教育，开发的软件将公开提供。A-DTIT将开发三种新的检测方法，共同应对在线监测海量数据流的根本挑战。第一种方法称为基于持久图的变点检测(PERCEPT)，它是一种新的非参数、拓扑感知算法，它扩展了拓扑数据分析中的最新工具，可以有效地监控高维数据流。第二种方法称为基于高斯过程的多源监测(MUM-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。第三种被称为条件自回归分布式(CARD)检测，是一种在线时空网络监测过程，它以分布和分散的方式利用邻近的空间信息。A-DTIT将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NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.