GV: Small: Collaborative Research: An Information-Theoretic Framework for Large-Scale Data Analysis and Visualization

GV：小型：协作研究：大规模数据分析和可视化的信息理论框架

• 批准号: 1017935
• 负责人: Chaoli Wang
• 金额: $ 20.73万
• 依托单位: Michigan Technological University
• 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 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1017935&HistoricalAwards=false
• 关键词: GV; Small; Collaborative; Research; Information

The growing power of supercomputers provides significant advancements to the scientists' capability to simulate more complex problems at greater detail, leading to high-impact scientific and engineering breakthroughs. To fully understand the vast amounts of data, scientists need scalable solutions that can perform complex data analysis at different levels of detail. Over the years, visualization has become an important method to analyze data generated by a variety of computationally intensive applications. The selection of visualization parameters and identification of important features, however, are mostly done in an ad-hoc manner. To enable the user to explore the data systematically and effectively, in this collaborative research effort involving the Ohio State University and the Michigan Technological University, the PIs explore an information-theoretical framework to evaluate the quality of visualization and guide the selection of algorithm parameters.The research team plans to develop a four-tier analysis framework based on information theory. The bottom tier of the framework consists of the components of information measures where data are modeled as probability distributions. Based on the information measurement components, in the tier two of the framework the most common visualization algorithms including isosurface extraction and flowline generation are evaluated and optimized to effectively reveal the most amount of information in the data. The PIs also investigate issues related to information measurement in image space and optimize the direct volume rendering results. The tier three of the framework is focused on the analysis of time-varying and multivariate data sets. Methods for identifying important spatio-temporal regions in time-varying data sets and to measure the information flow in multivariate data sets to identify the causal relationship among different variables will be developed. In the fourth tier of the framework, the information theory is used to assess the quality of different levels of detail in multi-resolution volumes and images, and to select the level of detail to optimize the visualization quality while satisfying the underlying performance constraints.The key accomplishment of this project will be the development of a rigorous information theory based solution to assist scientists in comprehending the vast amounts of data generated by large-scale simulations and effective visualizations. To target the research at real world applications, the PIs are collaborating with the combustion scientists at Sandia National Laboratories who are at the forefront of their field to employ extreme-scale computing to solve the most challenging problems. The four-tier information-theoretic framework will be implemented using the Visualization Toolkit (VTK), which is to be released to general users. New algorithms and techniques developed in the project will be disseminated through the project web site (http://www.cse.ohio-state.edu/~hwshen/Research/NSF_GV2010), presentations at the annual visualization and application-specific conferences that the PIs have been actively participating in. Dissemination plan will also includes reaching general audiences through news, stories, and presentations to enhance their understanding and appreciation of the value of visualization. This project provides training to graduate, undergraduate, and underrepresented students in the area of computational science and large-scale data analysis and visualization.

超级计算机的日益强大为科学家们提供了重大进步，使他们能够更详细地模拟更复杂的问题，从而实现高影响力的科学和工程突破。 为了充分理解大量的数据，科学家需要可扩展的解决方案，可以在不同的细节级别执行复杂的数据分析。 多年来，可视化已成为分析各种计算密集型应用程序生成的数据的重要方法。然而，可视化参数的选择和重要特征的识别主要是以特别的方式完成的。 为了使用户能够系统有效地探索数据，在这项由俄亥俄州州立大学和密歇根理工大学共同参与的研究中，PI们探索了一个信息理论框架，以评估可视化的质量并指导算法参数的选择。研究小组计划开发一个基于信息理论的四层分析框架。 框架的底层由信息度量的组件组成，其中数据被建模为概率分布。 基于信息度量组件，在框架的第二层中，最常见的可视化算法包括等值面提取和流线生成进行了评估和优化，以有效地揭示数据中的最大信息量。 PI还研究了与图像空间中的信息测量相关的问题，并优化了直接体绘制结果。该框架的第三层侧重于分析时变和多变量数据集。将制订各种方法，以查明时变数据集中的重要时空区域，并衡量多变量数据集中的信息流，以查明不同变量之间的因果关系。 在该框架的第四层中，信息论用于评估多分辨率体积和图像中不同细节级别的质量，该项目的主要成就是开发一个严格的基于信息理论的解决方案，以帮助科学家理解大量的通过大规模模拟和有效可视化生成的数据。 为了将研究目标锁定在真实的世界应用上，PI正在与桑迪亚国家实验室的燃烧科学家合作，他们处于该领域的最前沿，采用极端规模的计算来解决最具挑战性的问题。四层信息理论框架将使用可视化工具包（VTK）来实现，该工具包将向一般用户发布。 该项目开发的新算法和技术将通过项目网站（http：//www.cse.ohio-state.edu/Cashhwshen/Research/NSF_GV2010）传播，并在PI积极参加的年度可视化和应用专题会议上进行介绍。传播计划还将包括通过新闻、故事和演示来接触普通受众，以增强他们对可视化价值的理解和欣赏。该项目为计算科学和大规模数据分析和可视化领域的研究生，本科生和代表性不足的学生提供培训。

项目成果

Visualization Laboratory at University of Notre Dame

• DOI: 10.1016/j.visinf.2020.09.001
• 发表时间: 2020-09
• 期刊: Vis. Informatics
• 作者: Chaoli Wang