Integrated Analysis and Display of Multivariate and Time- Dependent Data

多元且随时间变化的数据的综合分析和显示

• 批准号: 9215145
• 负责人: Lambertus Hesselink
• 金额: $ 55万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-09-01 至 1997-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9215145&HistoricalAwards=false
• 关键词: Integrated; Analysis; Display; Multivariate; Time

9215145 Hesselink Both experimental and numerical techniques are becoming more sophisticated leading towards very large multivariate, time-dependent datasets describing complex physical phenomena. Visualization approaches for analysis and display of these datasets are currently inadequate, in particular when dealing with tensor data. The project addresses the need for better understanding of these datasets by investigating an integrated approach based on the triad of enabling technologies of feature extraction, data compression, and immersive environments. Three-dimensional datasets of increased complexity: tensor data and time-dependent fields will be displayed and analyzed. Most of the research effort is aimed at understanding the dynamical properties of physical systems that give rise to these large multivariate datasets. Therefore, techniques used for visualizing steady fields must be extended in order to visualize time-dependent data. Time-dependent data may be handled by animation of the individual time steps. Processing time for animations, however, are notoriously long. Therefore, data compression is being investigated at several stages of the integrated visualization process. In the analysis component, data compression of tensor fields is based on extracting and displaying hyperparticle paths using tensor field topology. In the display component, time-dependent fields may be analyzed and displayed using advanced data compression techniques such as vector clustering and 4-D transforms. Finally, the results of these analysis are displayed with tools from immersive environments, data compression techniques applied to rendering approaches, and other new technologies. *** v s t @ j l WINMINE HLP @ j J 1 WINTUTORDAT @ j j WRITE HLP @ j k CALC EXE @ j @ CALC HLP @ j F CALENDARHLP @ j P CANYON MID @ j CHITZ BMP @ j ı M DING WAV @ j N- 9215145 Hesselink Both experimental and numerical techniques are becoming more sophisticated leading towards very large multiv K 7 $ $ $ ( F / K / 1 Courier Symbol & Arial 5 Courier New Z " h lc lc r ( S R:\WW20USER\ABSTRACT.DOT abstract Alicia E. Harris Alicia E. Harris

实验和数值技术都变得越来越复杂，导致非常大的多变量，时间依赖性数据集描述复杂的物理现象。 这些数据集的分析和显示的可视化方法目前是不够的，特别是在处理张量数据。 该项目通过研究基于特征提取、数据压缩和沉浸式环境三位一体的使能技术的集成方法，满足了更好地理解这些数据集的需求。 复杂性增加的三维数据集：张量数据和时间相关字段将被显示和分析。 大部分的研究工作是为了了解物理系统的动力学特性，从而产生这些大型的多元数据集。 因此，用于可视化稳定场的技术必须扩展，以可视化随时间变化的数据。 时间相关数据可以通过各个时间步长的动画来处理。 然而，动画的处理时间是出了名的长。 因此，在综合可视化过程的几个阶段正在研究数据压缩。 在分析组件中，张量场的数据压缩是基于使用张量场拓扑提取和显示超粒子路径。 在显示组件中，可以使用诸如矢量聚类和4-D变换之类的高级数据压缩技术来分析和显示时间相关字段。 最后，这些分析的结果显示与沉浸式环境中的工具，数据压缩技术应用于渲染方法，和其他新技术。 *** v S t @ j l WINMINE HLP @ j J 1 WITUTORDAT @ j j 写 HLP @ j K CALC EXE @ j @ CALC HLP @ j F日历 @ j P峡谷MID @ j 奇茨 BMP @丁俊晖 WAV @ j N- 9215145 Hesselink实验和数值技术都变得越来越复杂，导致非常大的多变量 K 7 $ $ $ ( F / K / 1件 Symbol Arial 5 Cash New Z " H lc lc R ( S R：\WW20USER\ABSTRACT.DOT摘要 艾丽西娅·E哈里斯·艾丽西娅·E.哈里斯