CAREER: Toward Effective Visualization of Large Scale Time-Varying Data

职业：实现大规模时变数据的有效可视化

• 批准号: 0346883
• 负责人: Han-Wei Shen
• 金额: --
• 依托单位: Ohio State University Research Foundation -DO NOT USE
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-02-15 至 2010-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0346883&HistoricalAwards=false
• 关键词: CAREER; Toward; Effective; Visualization; Large

New challenges for computational scientists have emerged in the past several years as the size of data generated from simulations has experienced an exponential growth. One major factor that is contributing to the growth of data size is the increasingly widespread ability to perform very large-scale time-varying simulations. This project aims to design efficient data processing and effective feature extraction techniques to facilitate large-scale time-varying data analysis.The specific goal of this research is to study the following fundamental problems in time-varying visualization algorithms: (1) Design spatio-temporal data encoding and management schemes to facilitate run-time browsing and analysis of data at arbitrary spatial and temporal scales, (2) Develop accelerated visualization algorithms to utilize spatial and temporal data coherence. Topics of interest include the design of space-efficient time-varying isosurface extraction algorithms, automatic level of detail algorithms for multi resolution time-varying volume rendering, and load balanced parallel data distribution and visualization schemes, (3) Design novel time-varying feature enhancement and tracking techniques. Specifically, focus is on the development of novel high dimensional volume projection techniques to highlight the progression and evolution of time-dependent features, and high dimensional geometric algorithms to track time varying is contours and interval volumes. Focus is also on the design of effective transfer functions used in volume rendering to extract salient time-dependent features

在过去的几年中，随着模拟生成的数据规模经历了指数级增长，计算科学家面临着新的挑战。 导致数据规模增长的一个主要因素是执行非常大规模时变模拟的能力越来越普遍。 本项目旨在设计高效的数据处理和有效的特征提取技术，以方便大规模时变数据分析。本研究的具体目标是研究时变可视化算法中的以下基本问题：（1）设计时空数据编码和管理方案，以便于在任意时空尺度上对数据进行运行时浏览和分析，（2）开发加速可视化算法，以利用空间和时间数据的一致性。 研究内容包括空间有效的时变等值面提取算法、多分辨率时变体绘制的自动细节层次算法、负载均衡的并行数据分布和可视化方案。（3）设计新颖的时变特征增强和跟踪技术。具体而言，重点是开发新的高维体积投影技术，以突出时间相关特征的进展和演变，以及跟踪时变轮廓和间隔体积的高维几何算法。 重点也是设计有效的传递函数用于体绘制提取显着的时间相关的功能