EAGER-DynamicData: Generative Statistical Modeling for Dynamic and Distributed Data

EAGER-DynamicData：动态和分布式数据的生成统计建模

• 批准号: 1462230
• 负责人: Jia Li
• 金额: $ 25万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1462230&HistoricalAwards=false
• 关键词: EAGER; DynamicData; Generative; Statistical; Modeling

This project will develop a novel paradigm of generative modeling for decentralized data. In the big data era, the enormous volume, and high variety and velocity of data raise new technical challenges. What can be substantially strengthened is in the area of statistical learning under the limitations imposed by distributed data collections, communication networks, and decentralized computing platforms. As an example, the size of the data can be so large in many engineering applications that a single computer cannot handle. Typical learning methods, however, expect training data to be static and can be handled by one computer. The generative modeling framework has been shown to be effective in incorporating prior knowledge and capturing statistical dependence among data residing on structured domains, e.g., time sequences for signals and spatial grids for images. These advantages suit well with data arising from natural phenomena and the needs of engineering systems. The project addresses constraints in storage and communication capacity, as well as the speed requirement of real-time analysis by advancing multi-scale statistical modeling consisting of a layer of data-level learning and a layer of model-level learning. Two doctoral students will be supported to conduct research at the interface of engineering and statistics. They will develop core methodologies, as well as practical algorithms and tools useful in a wide range of engineering disciplines.The goal of this project is to propose new approaches in statistical learning for distributed and dynamic data subject to constraints of communication networks and the decentralized architecture of computing platforms. In particular, multi-scale statistical modeling for learning from distributed and dynamic data will be advanced. At the data-level, modeling is performed at decentralized computing sites. These models serve as a highly compact description of the data, retaining key information for learning. To consolidate the models acquired at distributed sites, only the models are communicated to a primary computer node. At the primary node, learning is performed directly on the models without regenerating data. An integrated investigation will be conducted on trade-offs between data and various computing resources such as CPU and storage. This project is transformative because of the fundamental nature of the problems, the unusual formulation of problems, and the interdisciplinary approaches. The usual paradigm of learning directly from data is transformed to multi-scale learning where statistical models become learning objects themselves. A suite of tools integrating methodologies in statistics and engineering will be developed and made available.

该项目将开发出用于分散数据的新型生成建模范式。在大数据时代，大量批量以及数据的种类繁多和速度引起了新的技术挑战。在分布式数据收集，通信网络和分散计算平台所施加的局限性下，可以实质上加强的是统计学习领域。例如，在许多工程应用程序中，数据的大小可能如此之大，以至于单个计算机无法处理。但是，典型的学习方法期望培训数据是静态的，并且可以通过一台计算机处理。生成建模框架已被证明可以有效地纳入与结构化域上的数据之间的统计依赖性，例如，图像的信号和空间网格的时间序列。这些优势非常适合由自然现象和工程系统需求产生的数据。该项目通过推进由数据级学习和模型级学习层组成的多尺度统计建模来解决存储和通信能力的限制以及实时分析的速度要求。将支持两名博士生在工程和统计的界面进行研究。他们将开发核心方法论，以及实用的算法和工具在广泛的工程学科中有用。该项目的目的是提出针对分布式和动态数据的统计学习中的新方法，并受到通信网络的约束和分散的建筑的约束计算平台。特别是，将提出用于从分布式和动态数据学习的多尺度统计建模。在数据级时，建模是在分散的计算位点进行的。这些模型是对数据的高度紧凑描述，保留了关键信息以进行学习。为了巩固在分布式站点上获取的模型，仅将模型传达给主计算机节点。在主节点上，学习直接在模型上执行，而无需再生数据。 将对数据与各种计算资源（例如CPU和存储）之间的权衡进行综合调查。由于问题的基本特性，问题的异常表述以及跨学科的方法，因此该项目具有变革性。直接从数据学习的通常学习范式转变为多规模学习，统计模型本身成为学习对象。 将开发并提供一套在统计和工程中集成方法的工具。