VISUALIZATION: Effective and Efficient Segmentation Frameworks for Scientific Data Exploration

计算能力、传感器和成像技术的迅速和实质性改进产生了越来越大的数据集。不再可能完全依赖传统的方法来进行交互式数据探索和可视化。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，这变得越来越重要。两种方法对于实现更高级别的数据探索至关重要：(1)依赖于表示多个近似级别的数据集的数据格式的分层数据探索和(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数据的定性行为，这反过来又可以导致数据的显著压缩，即，提取的片段或特征的几何表示通常比原始数据更具存储效率。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，支持通过分段进行更高效和有效的数据探索。复杂的科学数据集代表了包含数十亿元素的物理现象。这些数据集是多值的、多维的、时变的，我们不能仅仅用传统的可视化技术来全面分析它们。在开发用于探索此类数据集的工具时，可以考虑各种范例，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，投资者的兴趣集中在某些房产持有的特定地区。因此，应该开发工具，使科学家能够指定感兴趣的属性，并相应地分割数据集。所提出的方法支持定义高级数据属性、有效地提取隐含数据以及对提取的数据进行有效的可视化表示。我们的框架针对的是多值时变数据集，例如，网格顶点可能有多个相关的标量、向量和张量。我们的目标是设计新的算法，支持数字鲁棒提取代表类似定性行为的区域（或这些区域的边界）。我们提出这种“分段”方法用于大规模数据集探索，因为我们认为有必要为科学家提供支持更高级别数据表示和实时系统行为的探索技术。挑战在于如何从给定的多值数据集生成“分段数据”，有效地存储分段数据，生成分段边界的边界，并显示这些边界。我们提出了一个集成方案，该方案支持分割的通用数据表示，并且可以应用于许多数据类型（标量，矢量或张量）。此外，我们将把分割框架与新的多分辨率技术相结合——多分辨率技术将支持在多个分辨率水平上提取和可视化分割数据和提取的段边界。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。新的框架将允许科学家定义、分割和跟踪有意义的派生数据区域。科学家将能够将注意力集中在数据集中携带最大信息内容的那些区域，我们框架的强大之处在于，它将有可能更有效地定义和提取这些区域。

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.