BIGDATA: Small: DA: Data Summarization, Analysis, and Triage for Very Large Scale Flow Fields

BIGDATA：小型：DA：超大规模流场的数据汇总、分析和分类

• 批准号: 1250752
• 负责人: Han-Wei Shen
• 金额: $ 72.73万
• 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: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1250752&HistoricalAwards=false
• 关键词: BIGDATA; Small; DA; Data; Summarization

Two major challenges are faced by computational scientists who routinely generate big data sets. The first is deciding what data are the most essential for analysis, given that only a small fraction of them can be retained. The second is transforming these data into information that conveys the most insight. As the size of simulation output continues to grow, the "save the data first, analyze them later" approach needs to be completely replaced with more aggressive data prioritization and reduction before any analysis can be done. In this project, core data analytics technologies are developed to facilitate effective data summarization, indexing, and triage for large-scale flow data. Fluid flow plays an important role in explaining many phenomena across a wide range of disciplines. To provide the scientists with a succinct view of the data content, and also organize the data and features based on their similarity and complexity, a graph-based model is developed to simultaneously reveal the major structure of the flow field, and to facilitate high performance and out-of-core flow line computation. We develop statistical and geometrical complexity measures for the flow lines to efficiently group and prioritize sub-regions in the vector field to allow efficient data access. To characterize the temporal complexity of flow fields, we develop time-varying analysis algorithms that allow for more detailed analysis of the data, and provide the user with flexible interface to quickly identify salient features.The development of the proposed integrated flow analysis and visualization framework initially targeted two applications, simulations of turbo machinery in aerodynamics, and study of Madden Julian Oscillation in climate modeling. As typical flow in turbo machinery is full of evolving shocks and vortical structures, visualization allows the designers to identify loss regions and complex flow features in a relatively short amount of time if these features can be identified automatically. To understand the phenomenon of Madden Julian Oscillation, as this phenomenon is strongly related to the convection of air, the flow analysis techniques developed under this project can be used to identify and track its locations and durations. Because the size of data generated by time-varying simulations can be prohibitively large, the proposed time-varying data reduction techniques allow scientists to focus on the most salient portion of the data. The key impact of this project is to make available a working and attractive solution to assist scientists to comprehend the vast amount of data generated by large-scale simulations. Through close collaboration with application scientists, the research ideas developed in this project into will be transformed into an open source software framework.

经常生成大数据集的计算科学家面临两个主要挑战。首先是考虑到只有一小部分数据可以保留，决定哪些数据对分析来说是最重要的。第二是将这些数据转化为传达最深刻见解的信息。随着模拟输出的规模不断增长，在进行任何分析之前，“先保存数据，后分析”的方法需要完全被更积极的数据优先级和缩减所取代。在本项目中，开发了核心数据分析技术，以促进大规模流量数据的有效数据汇总、索引和分类。流体流动在解释许多学科中的许多现象方面起着重要作用。为了使科学家能够简洁地了解数据内容，并根据数据的相似性和复杂性对数据和特征进行组织，开发了一个基于图的模型，以同时揭示流场的主要结构，并促进高性能和岩心外流线的计算。我们开发了流线的统计和几何复杂性措施，以有效地分组和优先考虑矢量场中的子区域，以允许有效的数据访问。为了表征流场的时间复杂性，我们开发了时变分析算法，允许对数据进行更详细的分析，并为用户提供灵活的界面来快速识别显著特征。提出的集成流动分析和可视化框架的开发最初针对两个应用，空气动力学中的涡轮机械模拟和气候建模中的Madden Julian振荡研究。由于涡轮机械中的典型流动充满了不断变化的激波和旋涡结构，如果能够自动识别这些特征，可视化使设计人员能够在相对较短的时间内识别损失区域和复杂的流动特征。为了理解马登朱利安振荡现象，因为这种现象与空气对流密切相关，在这个项目下开发的流动分析技术可以用来识别和跟踪它的位置和持续时间。由于时变模拟产生的数据规模可能非常大，因此提出的时变数据缩减技术使科学家能够专注于数据中最突出的部分。这个项目的关键影响是提供一个可行的和有吸引力的解决方案，以帮助科学家理解大规模模拟产生的大量数据。通过与应用科学家的密切合作，本项目开发的研究思路将转化为开源软件框架。