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: 1017635
• 负责人: Han-Wei Shen
• 金额: $ 29.21万
• 依托单位: Ohio State 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=1017635&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.

超级计算机的不断增长为科学家们更详细地模拟更复杂问题的能力提供了重大进步，从而导致了高影响力的科学和工程突破。 为了充分了解大量数据，科学家需要可扩展的解决方案，这些解决方案可以在不同级别的细节上执行复杂的数据分析。 多年来，可视化已成为分析通过各种计算密集型应用程序生成的数据的重要方法。但是，可视化参数的选择和重要特征的识别主要以临时方式完成。 为了使用户能够系统地有效地探索数据，在涉及俄亥俄州立大学和密歇根州技术大学的这项协作研究工作中，PIS探索了一个信息理论框架，以评估可视化质量并指导算法参数的选择。基于信息论的四层分析框架，该研究团队计划基于信息论。 该框架的底层由信息测量的组成部分组成，其中数据被建模为概率分布。 基于信息测量组件，在框架的第二层中，评估并优化了最常见的可视化算法，包括等法提取和流线生成，以有效地揭示数据中最多的信息。 PI还研究了与图像空间中信息测量相关的问题，并优化了直接量渲染结果。该框架的第三层集中在分析时变和多元数据集上。将开发用于确定随时间变化的数据集中重要的时空区域的方法，并测量多元数据集中的信息流以识别不同变量之间的因果关系。 在框架的第四层中，信息理论用于评估多分辨率卷和图像中不同细节的质量，并选择细节级别以优化可视化质量，同时满足基础性能的限制。该项目的关键成就将是基于基于信息的科学家的开发，从而帮助科学家开发大量的数据生成量，并有效地实现了数据生成的量。 为了针对现实世界应用的研究，PIS与桑迪亚国家实验室的燃烧科学家合作，他们处于其领域的最前沿，以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）实现，该工具包（VTK）将发布给普通用户。 该项目中开发的新算法和技术将通过项目网站（http://www.cse.ohio-state.edu/~hwshen/research/research/nsf_gv2010）进行分解，在年度可视化和应用特定的会议上，PIS的介绍还包括在内，并将其包括在内，并将其包括在内。了解和欣赏可视化的价值。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，本科和代表性不足的学生提供培训。