SGER: Metadata-Driven Approach to Discovery-Oriented Exploration of Massive Data Sets

SGER：元数据驱动的海量数据集发现导向探索方法

• 批准号: 0841031
• 负责人: Ruth West
• 金额: --
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2010-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0841031&HistoricalAwards=false
• 关键词: SGER; Metadata; Driven; Approach; Discovery

Multi-scale, multi-modal, multi-site science, such as terrestrial observatories and other dynamic and adaptive sensor-based application, high resolution sub-cellular imaging, or genomic analyses of communities of organisms, builds large data sets that can serve as the basis for in silico exploration and analysis. Often the data collections represent a wide range of disciplines, yet there are often a limited number of interpretations, guided by an individual scientist?s expertise. Intuitive exploration that transcends disciplinary boundaries and expertise could enhance the process of interdisciplinary collaboration, as well as drive the process of discovery in new directions. This proposal seeks to develop a dynamic metadata-based approach for intuitive, interactive, and immersive multi-scale, multi-modal data exploration application to a wide range of data sets and their associated metadata. The original focus will be on metagenomics data from the Global Ocean Survey. The development of the environment will enable a different dynamic sorting and sifting of data rather than relying solely on known or expected features. Such open-ended exploration occurs not only in observational science, but also in tasks ranging from network intrusion detection. Students will be engaged in the research and collaboration, with dissemination in public cultural activities, as well as through more traditional academic venues.

多尺度，多模式的多站点科学，例如地面观测器以及其他动态和自适应传感器的应用，高分辨率的亚细胞成像或对生物体群落社区的基因组分析，可以构建大型数据集，这些数据集可以作为硅胶勘探和分析的基础。 通常，数据收集代表了广泛的学科，但是在个人科学家的专业知识的指导下，通常会有有限的解释。 超越学科界限和专业知识的直观探索可以增强跨学科合作的过程，并将发现过程推向新的方向。该提案旨在开发一种基于动态元数据的方法，用于直观，交互式和身临其境的多尺度，多模式数据探索应用程序，以对广泛的数据集及其相关的元数据进行。 最初的重点将放在全球海洋调查的宏基因组学数据上。 环境的开发将使数据进行不同的动态排序和筛分，而不是仅依靠已知或预期的功能。 这种开放式探索不仅发生在观察科学中，而且发生在网络入侵检测等任务中。 学生将通过公共文化活动以及更传统的学术场所进行研究和合作。