CRI: Planning - A Massive and Heterogeneous Data Repository for Computing Research on the Gulf of Mexico

CRI：规划 - 用于墨西哥湾计算研究的大规模异构数据存储库

• 批准号: 0708596
• 负责人: Long-zhuang Li
• 金额: --
• 依托单位: Texas A&M University Corpus Christi
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2010-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0708596&HistoricalAwards=false
• 关键词: CRI; Planning; Massive; Heterogeneous; Data

The goal of this planning project is to assemble a multi-disciplinary and multi-institutional team of computer scientists, biologists, marine scientist, and environmental researchers to develop a CRI large community resource proposal for building a massive and heterogeneous data repository. The data repository will consist of biological, ecological, and geographical information science (GIS) related data that will enhance cutting-edge computing research in the Gulf of Mexico (GOM). The repository will incorporate new data, but will largely consist of existing data from a variety of ocean observing systems around the GOM and the Harte Research Institute (HRI) at Texas A&M University-Corpus Christi (TAMU-CC). The development of the repository will be an interdisciplinary effort involving researchers from computer science, GIS, marine science, biological science, and environmental science, etc. The unique coastal location of TAMU-CC is an ideal environment for the development of the repository. The primary activities of this planning project are: (1) soliciting and analyzing detailed requirements from the research community; (2) examining and identifying data sets from different GOM ocean observation systems and the HRI; and (3) investigating the alternative repository architectures. The research objective is to provide the research community with multi-dimensional data sets for managing, analyzing, storing, sharing, visualizing, querying, and disseminating. The inherent dimensionality of the repository will stimulate the development of novel research supporting integrated research in a wide range of computing areas. The integration of data from different ocean monitoring systems and the HRI will lead to further renewal of demands for the development of novel technologies designed for the organization and mining of the data to enhance computing research. The heterogeneous nature of weather, oceanic and coastal observation, and biological and ecological data?ranging from free-text, image, sound, video, and sensor readings to GIS airborne multi-spectral imaging?provides an excellent research resource and test-bed for development of novel computing technologies. Areas of possible concentration are data integration, database modeling, information storage and retrieval, data mining, human computer interaction, modeling and forecasting, visualization, parallel processing, etc. The integrated repository will not only aid research in the GOM by providing enormous amount of raw data, but also become a valuable resource for the public, local government officials, scientists, natural resource managers and educators. Once completed, the repository will serve as a data resource for students in interdisciplinary science courses. Theproject will also have strong potential for extended outreach to other institutions in the GOM, including a K-12 outreach component.

该计划项目的目的是组装一个由计算机科学家，生物学家，海洋科学家和环境研究人员组成的多学科和多机构团队，以开发一个CRI大型社区资源建议，用于建立大量和异构的数据存储库。数据存储库将由生物学，生态和地理信息科学（GIS）相关数据组成，这些数据将增强墨西哥湾（GOM）的尖端计算研究。该存储库将包含新数据，但很大程度上将由来自GOM周围的各种海洋观察系统和德克萨斯州A＆M大学 - 科尔普斯·克里斯蒂（TAMU-CC）的HARTE研究所（HRI）的现有数据组成。 存储库的发展将是跨学科的努力，涉及计算机科学，GIS，海洋科学，生物科学和环境科学等的研究人员。Tamu-CC的独特沿海位置是开发存储库的理想环境。该计划项目的主要活动是：（1）征求和分析研究界的详细要求； （2）检查和识别来自不同GOM海洋观察系统和HRI的数据集； （3）调查替代存储库体系结构。研究目标是为研究社区提供用于管理，分析，存储，共享，可视化，查询和传播的多维数据集。存储库的固有维度将刺激新的研究的发展，支持广泛的计算领域的综合研究。 来自不同海洋监测系统和HRI的数据集成将进一步续签针对组织和挖掘数据的新技术开发的需求，以增强计算研究。天气，海洋和沿海观察的异质性质以及生物学和生态数据范围从自由文本，图像，声音，视频，视频和传感器读数到GIS机载多光谱成像？提供了出色的研究资源和测试床，用于开发新型计算技术。可能集中的领域是数据集成，数据库建模，信息存储和检索，数据挖掘，人机互动，建模和预测，可视化，并行处理等。集成的存储库不仅将通过提供大量的原始数据来帮助GOM中的研究，而且还将成为公共，地方政府官员，科学官员，科学管理者，自然资源和教育者，自然界和教育者的宝贵资源。 完成后，存储库将作为跨学科科学课程的学生的数据资源。该项目还将有强大的潜力向GOM中的其他机构（包括K-12外展部分）扩展外展。