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

该项目将为分散的数据开发一种新的生成性建模范例。在大数据时代，海量、高种类、高速度的数据提出了新的技术挑战。在分布式数据收集、通信网络和分散计算平台的限制下，可以大大加强的是统计学习领域。例如，在许多工程应用中，数据的大小可能如此之大，以至于单台计算机都无法处理。然而，典型的学习方法期望训练数据是静态的，并且可以由一台计算机处理。产生式建模框架已被证明在结合先验知识和捕获驻留在结构化领域的数据之间的统计相关性方面是有效的，所述结构化领域例如信号的时间序列和图像的空间网格。这些优点与自然现象产生的数据和工程系统的需要很好地匹配。该项目通过推进由一层数据学习和一层模型学习组成的多尺度统计建模，解决了存储和通信能力方面的限制以及实时分析的速度要求。支持两名博士生在工程学和统计学的界面上进行研究。他们将开发核心方法，以及在广泛的工程学科中有用的实用算法和工具。该项目的目标是在通信网络和计算平台的分散架构的限制下，为分布式和动态数据的统计学习提出新的方法。特别是，将推进从分布式和动态数据中学习的多尺度统计建模。在数据级别，建模在分散的计算站点执行。这些模型是对数据的高度紧凑描述，保留了用于学习的关键信息。为了合并在分布式站点获取的模型，仅将模型传送到主计算机节点。在主节点，直接在模型上执行学习，而不重新生成数据。将对数据和各种计算资源(如CPU和存储)之间的权衡进行综合调查。这个项目之所以具有变革性，是因为问题的根本性质、问题的不寻常表述以及跨学科方法。通常的直接从数据学习的范例被转变为多尺度学习，其中统计模型本身就成为学习对象。将开发并提供一套综合统计和工程方法的工具。

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.