EAGER: Exploring Compressive Sampling for Extreme-Scale Data Visualization

EAGER：探索超大规模数据可视化的压缩采样

• 批准号: 1048508
• 负责人: Alireza Entezari
• 金额: $ 8.5万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1048508&HistoricalAwards=false
• 关键词: EAGER; Exploring; Compressive; Sampling; Extreme

AbstractThis EAGER aims to provide practical evidence of feasibility for a larger project called instance-optimal sampling. The instance-optimal sampling is a foundational framework for optimal representation of extreme-scale (scattered, unstructured, and structured) datasets. Using the dense polytope packing algorithms, the instance-optimal sampling framework develops strategies for sampling a given dataset at the optimally minimal sampling rate. The instance-optimal representation is derived based on the multidimensional notion of Nyquist frequencies; therefore, this approach is best complemented with the compressive sampling (CS) methods that exploit the sparsity of a dataset to reduce the sampling rate significantly below the Nyquist rate with no loss of information.The main motivation in this research is that the synergy of compressive sampling and instance-optimal sampling would potentially allow the reduction of an extreme-scale dataset to sizes that are logarithmically proportional to number of samples in that dataset and linearly proportional to its sparsity. The research addresses the computational efficiency issue of sparse reconstruction for volumetric and time-varying datasets, which can lay the basis for applying CS to computer graphics problems. The main challenge is the computational cost of the reconstruction algorithm for 3-D or time-varying data. This research examines the feasibility of adopting a tensor-product approach to compressive sampling.

本文旨在为一个更大的项目--实例最优抽样提供可行性的实际证据。实例最优采样是极端规模(分散的、非结构化的和结构化的)数据集的最佳表示的基本框架。使用密集多面体填充算法，实例最优采样框架制定了以最佳最小采样率对给定数据集进行采样的策略。实例最优表示是基于Nyquist频率的多维概念，因此，该方法与压缩采样(CS)方法是最好的补充，压缩采样方法利用数据集的稀疏性，在不损失信息的情况下将采样率显著降低到显著低于Nyquist速率。本研究的主要动机是压缩采样和实例最优采样的协同作用潜在地允许将极端规模的数据集的大小减少到与该数据集中的样本数量成对数正比、与其稀疏成线性比例。研究了体和时变数据集的稀疏重建的计算效率问题，为将CS应用于计算机图形学问题奠定了基础。主要的挑战是三维或时变数据重建算法的计算成本。本研究探讨采用张量积方法进行压缩抽样的可行性。