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

许多重要的大数据问题可以被建模为节点映射到物理空间的网络。为了解决这些问题，该项目研究了一种名为空间关系网络的新型网络抽象。这种抽象针对的是具有独特大数据挑战的广泛类别的应用程序，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，网络组件建模的关系基本上对应于影响和信息流的非空间模式，而空间关系代表不同类型的异质交互。例如，在人类的连接体中，边缘对应于大脑中一起放电的区域，而空间邻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上的重要分析问题开发模型、方法和软件，并在一系列现实世界问题上进行验证。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。这些领域从在线交易中的新型欺诈检测算法，到阿尔茨海默氏症和帕金森氏症等神经退行性疾病的发病和发展研究。除了其研究和应用的影响，该项目还瞄准了一些教育贡献。其中包括数据科学课程的开发、大数据分析的在线模块、大型会议上关于空间关系网络抽象的讲习班和教程，以及本科生研究的机会。该项目为空间关系网络奠定了基础，使其成为能够进行新颖分析的基础和可扩展的大数据抽象。它的目标是得到空间网络的高效计算表示，空间关系网络的高性能算法，以及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的建模和分析。项目目标包括在分析人类连接项目、艾伦大脑图谱和基于位置的社交网络的数据时，对选定的应用程序进行软件开发和全面验证。

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.