CIF: Medium: Data Science: Analytics for Unstructured and Distributed Data

到岸价：中等：数据科学：非结构化和分布式数据的分析José M F莫拉和Soummya KarAbstract融合技术的完美风暴导致了数字格式数据的冲击，准备被获取，存储，访问，传输和处理。这项研究开发了定量分析，适用于传统工程或科学之外的许多新领域，如社交网络或城市环境中产生的数据。这些数据是非结构化的吗？它不再是单一的时间序列或单一的图像，并且不能自然地安排在矢量或表格中。数据是分布式的？它源自可能分散在大的物理空间上的许多不同的代理（例如，都市区）。为了处理非结构化、分布式的大数据，将传统的信号处理方法扩展为基于图的分布式信号处理方法（DSPG），将两个图与数据关联起来：1）图？物理图？哪些节点索引数据，哪些物理边捕获数据之间的关系或依赖关系; 2）？网络？图，可能不同于物理图，其节点索引分布式处理单元，并且其网络边表示这些单元之间的（本地）通信信道。为了将DSPG分析扩展到处理分布式大数据分析，研究人员解决了以下挑战：1）发现底层物理图的结构; 2）设计共识+创新可扩展的分布式算法，以准确处理分布式大数据。重要分析的例子包括：计算y = Hx的前向滤波，其中H是图滤波器或图变换，x是与代理相关的数据;以及计算线性系统Hx = y的解的逆问题，其中H和y是给定的，而x是重建的。

CIF: Medium: Data Science: Analytics for Unstructured and Distributed DataJosé M F Moura and Soummya KarAbstractThere has been a perfect storm of convergent technologies leading to an onslaught of data in digital format, ready to be acquired, stored, accessed, transmitted, and processed. This research develops quantitative analytics that are appropriate for data arising in many novel areas like social networks or urban environments, outside traditional engineering or science. This data is unstructured?it is no longer a single time series or a single image and cannot be naturally arranged in a vector or table. The data is distributed?it originates from many different agents, possibly scattered over a large physical space (e.g., a metro area). To process unstructured and distributed Big Data, the research extends traditional signal processing methods to distributed signal processing on graphs (DSPG) by associating two graphs with the data: 1) the ?physical graph? whose nodes index the data and whose physical edges capture the relations or dependencies among the data; and 2) the ?cyber? graph, possibly different from the physical graph, whose nodes index distributed processing units and whose cyber edges represent (local) communication channels among these units. To extend DSPG analytics to process distributed Big Data analytics, the investigators address the following challenges: 1) discover the structure of the underlying physical graph; and 2) design consensus+innovations scalable distributed algorithms to accurately process the distributed Big Data. Examples of important analytics include: forward filtering that computes y = Hx, where H is a graph filter or graph transform and x is the data associated with the agents; and inverse problems that compute solutions to linear systems Hx = y, where H and y are given, while x is reconstructed.