BIGDATA: Collaborative Research: F: From Data Geometries to Information Networks

大数据通常来自多个来源，包含来自不同观察者的对同一对象、空间或现象的多个（通常是部分）“视图”的集合。 从这些数据集中有力地提取信息需要对大量数据集进行联合分析。 该项目正在开发一种新的几何框架，用于建模，结构检测和从大量相关数据集中提取信息，重点是数据之间的关系。 虽然这种方法显然适用于具有清晰几何特征的数据（例如，图像中的对象），这项工作也适用于计算机网络（识别网络中的常见结构）和大规模开放式在线课程作业数据（自动携带其他学生作业中类似问题的评分员注释）等不同的数据集。该新颖的框架基于所考虑的对象（点云，图形，图像等）之间的地图构建，以及对地图网络的分析，作为提取信息、生成数据的潜在模型以及传输或推断功能/语义信息的一种方式。这些任务定义了数据集之间的映射处理的新领域，并需要具有数学中的泛函分析，非凸优化和同调代数以及计算机科学中的几何算法，机器学习，优化和近似算法的新思想的工具集。 复杂的算法技术攻击的框架内出现的大规模非线性优化问题也将进行调查。

Big Data often results from multiple sources, giving collections that contain multiple, often partial, "views" of the same object, space, or phenomenon from various observers. Extracting information robustly from such data sets calls for a joint analysis of a large collection of data sets. The project is developing a novel geometric framework for modeling, structure detection, and information extraction from a collection of large related data sets, with an emphasis on the relationships between data. While this approach clearly applies to data with a clear geometric character (e.g., objects in images), the work is also applied to datasets as diverse as computer networks (identifying common structure in subnets) and Massive Open Online Course homework data (automatically carrying grader annotations to similar problems in other students' homeworks).The novel framework is based on the construction of maps between the objects under considerations (point clouds, graphs, images, etc...), and on the analysis of the networks of maps that result as a way of extracting information, generating latent models for the data, and transporting or inferring functional / semantic information. These tasks define a new field of map processing between data sets and require tool sets with new ideas from functional analysis, non-convex optimization, and homological algebra in mathematics, and geometric algorithms, machine learning, optimization, and approximation algorithms in computer science. Sophisticated algorithmic techniques for attacking the large-scale non-linear optimization problems that emerge within the framework will also be investigated.