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

CIF：媒介：数据科学：非结构化和分布式数据分析

• 批准号: 1513936
• 负责人: Jose Moura
• 金额: $ 118.94万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1513936&HistoricalAwards=false
• 关键词: CIF; Medium; Data; Science; Analytics

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.

媒介：数据科学：非结构化和分布式数据的分析技术已经出现了一场完美的融合技术风暴，导致了数字格式数据的冲击，这些数据随时可以被获取、存储、访问、传输和处理。这项研究发展了定量分析，适用于在传统工程或科学之外的许多新领域，如社会网络或城市环境中产生的数据。这些数据是非结构化的？它不再是单个时间序列或单个图像，也不能自然地排列在矢量或表中。数据是分布式的？它起源于许多不同的媒介，可能分散在一个大的物理空间（例如，一个大都市地区）。为了处理非结构化和分布式的大数据，本研究将传统的信号处理方法扩展到分布式信号处理图（DSPG），将两个图与数据关联起来：1)？实物图?其节点索引数据，其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，其网络边表示这些单元之间的（本地）通信通道。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+创新可扩展分布式算法，精准处理分布式大数据。重要分析的例子包括：计算y = Hx的前向过滤，其中H是图形过滤器或图形转换，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是已知的，而x是重构的。