VISUALIZATION: Effective and Efficient Segmentation Frameworks for Scientific Data Exploration

可视化：科学数据探索的有效和高效的分割框架

• 批准号: 0222909
• 负责人: Kenneth Joy
• 金额: $ 54.7万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-10-01 至 2006-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0222909&HistoricalAwards=false
• 关键词: VISUALIZATION; Effective; Efficient; Segmentation; Frameworks

Rapid and substantial improvements in computing power and sensor and imaging technologies produce data sets of ever increasing size. No longer is it possible to rely exclusively on traditional approaches to interactive data exploration and visualization. It is becoming increasingly important to provide technology that enables scientists to analyze their data interactively at higher levels of abstraction. Two approaches arecrucial to making possible higher-level data exploration: (1) hierarchical data exploration relying on a data format representing a data set at multiple approximation levels and (2) segmentation- (or feature-based) data exploration. Approach (2) as becoming increasingly important as it allows scientists to study qualitative behavior of their data, which, in turn, can lead to significant compression of data, i.e., the geometrical representations for extracted segments or features are typically much more storage-efficient than original data. We will do research based on approach (2), and our goal is to devise an entire new framework supporting more efficient and effective data exploration via segmentation. Complex scientific data sets represent physical phenomena with billions of elements. These data sets are multi-valued, muti-dimensional and time-varying, and we are no longer able to fully analyze them with merely traditional visualization technology. One can consider various paradigms when developing tools for the exploration of such data sets, and onemust consider that scientists rarely needs to examine entire data sets. Typically, the interest is in particular regions where certain properties hold. Tools should therefore be developed that allow a scientist to specify the properties of interest, and to segment data sets accordingly.The proposed approach supports the definition of higher-level data properties, the efficient extraction of the implied data, and the effective visual representation of the extracted data. Our framework is aimed at multi-valued time-varying data sets, where, for example, grid vertices might have multiple associated scalar, vector and tensor quantities.Our goal is to devise new algorithms that support the numerically robust extraction of regions (or boundaries of these regions) that represent similar qualitative behavior. We propose this ''segmentation'' approach to massive data set exploration as we believe that it is a necessary to provide scientists with exploration technology that supports higher-level data representation coupled with real-time systembehavior.The challenge is to generate ''segmented data'' from given multi-valued data sets, store the segmented data efficiently, generate the boundaries of segment boundaries, and display these boundaries. We propose an integrated scheme that supports common data presentation for segmentation and that can be applied to a number of data types (scalar, vector or tensor).In addition, we will combine the segmentation framework with new multiresolution techniques---multiresolution techniques that shall support the extraction and visualization of segmented data and extracted segment boundaries at multiple resolution levels. The need for new tools to explore multi-valued time-varying data sets is paramount. The innovative framework we propose to develop will augment current data exploration paradigms. The new framework will allow scientists to define, segment and track meaningful derived data regions.Scientists will be able to focus attention to those areas in a data set that carry largest information content, and the power of our framework lies in the fact that it will be possible to more effectively define and extract these area.

计算能力、传感器和成像技术的快速和实质性改进产生了规模不断增加的数据集。不再可能完全依赖传统的交互式数据探索和可视化方法。越来越重要的是提供技术，使科学家能够在更高的抽象层次上交互式地分析他们的数据。有两种方法有助于实现更高级别的数据探索：（1）分层数据探索，依赖于在多个近似级别上表示数据集的数据格式;（2）分段（或基于特征）的数据探索。方法（2）变得越来越重要，因为它允许科学家研究其数据的定性行为，这反过来又会导致数据的显著压缩，即，所提取的段或特征的几何表示通常比原始数据存储效率高得多。 我们将基于方法（2）进行研究，我们的目标是设计一个全新的框架，通过分割支持更高效和有效的数据探索。复杂的科学数据集代表了数十亿元素的物理现象。这些数据集具有多值、多维和时变的特点，仅仅依靠传统的可视化技术已经无法对其进行全面的分析。 在开发用于探索这些数据集的工具时，可以考虑各种范式，并且必须考虑到科学家很少需要检查整个数据集。通常情况下，兴趣是在特定地区，其中某些属性持有。因此，应该开发工具，让科学家指定的属性的兴趣，并分段数据setsources.The建议的方法支持更高级别的数据属性的定义，有效的提取隐含的数据，并有效的可视化表示提取的数据。我们的框架是针对多值时变数据集，其中，例如，网格顶点可能有多个相关的标量，矢量和张量quantities.我们的目标是设计新的算法，支持数值鲁棒提取区域（或这些区域的边界），代表类似的定性行为。我们提出这种“分段"方法来探索海量数据集，因为我们相信，这是一个必要的，以提供科学家的探索技术，支持更高层次的数据表示与实时系统行为相结合。挑战是从给定的多值数据集生成”分段数据“，有效地存储分段数据，生成段边界的边界，并显示这些边界。我们提出了一个集成的方案，支持共同的数据表示分割，并可以应用到一些数据类型（标量，矢量或张量）。此外，我们将联合收割机分割框架与新的多分辨率技术--多分辨率技术，应支持分割数据和提取的段边界在多个分辨率水平的提取和可视化。 需要新的工具来探索多值时变数据集是至关重要的。我们建议开发的创新框架将增强当前的数据探索范式。新框架将允许科学家定义、分割和跟踪有意义的衍生数据区域。科学家将能够将注意力集中在数据集中承载最大信息内容的区域，而我们框架的强大之处在于可以更有效地定义和提取这些区域。