BIGDATA: F: Models, Algorithms, and Software for Spatial-Relational Networks

许多重要的大数据问题可以建模为网络，其中节点在物理空间中被映射。为了解决这些问题，该项目研究了一种新的网络抽象，称为空间关系网络。这种抽象针对的是具有独特大数据挑战的广泛应用，包括金融欺诈、流行病学和系统工程。空间关系网络的显著特征是，网络组成部分建立的关系从根本上对应于影响和信息流的非空间模式，而空间关系则代表了一种不同类型的异质相互作用。例如，在人类的连接组中，边缘对应于大脑中一起放电的区域，而空间接近反映了病理生理方面，如病变的存在。该项目旨在为空间关系网络的重要分析问题开发模型、方法和软件，并在一系列现实问题上验证它们。该项目在许多应用中实现了全新的分析技术，同时显著提高了其他分析技术的准确性和效率。这些研究范围从在线交易中的新型欺诈检测算法，到研究阿尔茨海默氏症和帕金森病等神经退行性疾病的发病和进展。除了其研究和应用的影响，该项目还针对一些教育贡献。这些措施包括开发数据科学课程、大数据分析在线模块、在主要会议上举办关于空间关系网络抽象的研讨会和教程，以及为本科生提供研究机会。该项目建立了空间关系网络的基础，作为一种基本的、可扩展的大数据抽象，使新的分析成为可能。它旨在为空间网络提供高效和有效的计算表示，为空间关系网络提供高性能算法，为节点关系和属性是动态的，但其空间位置是固定的动态空间关系网络建模和分析。项目目标包括软件开发和对选定应用程序的综合验证，这些应用程序分析来自人类连接组项目、艾伦大脑图谱和基于位置的社交网络的数据。

Many important big-data problems can be modeled as networks in which nodes are mapped in physical space. To address these problems, the project investigates a novel network abstraction called a spatial-relational network. This abstraction targets a broad class of applications with unique big-data challenges, including financial fraud, epidemiology, and systems engineering. The distinguishing feature of spatial-relational networks is that the network component models relationships that correspond to fundamentally non-spatial modes of influence and information flow, whereas the spatial relationships represent a different type of heterogeneous interaction. As an example, in the human connectome, edges correspond to regions of the brain that fire together, whereas spatial proximity reflects patho-physiological aspects such as the existence of a lesion. This project aims to develop models, methods, and software for important analysis problems on spatial-relational networks as well as validate them on a range of real-world problems. The project enables fundamentally new analytic techniques in many applications while significantly enhancing the accuracy and efficiency of analyses techniques in others. These range from novel fraud detection algorithms in online transactions to study of onset and progression of neurodegenerative diseases such as Alzheimer's and Parkinson's. Beyond its research and applications' impact, the project also targets a number of educational contributions. These include development of a curriculum for data sciences, online modules on big-data analytics, workshops and tutorials on the spatial-relational network abstraction at major conferences, and opportunities for undergraduate research.This project establishes the foundations of spatial-relational networks as a fundamental and scalable big-data abstraction that enables novel analyses. It aims to derive efficient and effective computational representations for spatial-networks, high-performance algorithms for spatial-relational networks, modeling and analysis of dynamic spatial-relational networks in which the node relations and attributes are dynamic, but their spatial locations are fixed. Project goals include software development and comprehensive validation on selected applications in the analysis of data from the Human Connectome Project, the Allen Brain Atlas, and location-based social networks.