I/UCRC: Site application to join I/UCRC known as CHMPR

I/UCRC：加入 I/UCRC 的网站申请（称为 CHMPR）

• 批准号: 1624605
• 负责人: Gregg Rothermel
• 金额: $ 29.85万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1624605&HistoricalAwards=false
• 关键词: UCRC; Site; application; join; known

Nearly every research field and industry sector is struggling with managing and extracting useful information from massive data sets, that are commonly referred to as Big Data. Modeling, data management, and analytics are key elements in understanding and using these data sets, and their importance in academia and industry is predicted to grow exponentially. One central challenge can be expressed as follows: Given specific analysis tasks and specific constraints on the available Big Data, how do we enable productive, efficient, and secure end-to-end value extraction from the data? To address this challenge, we propose to join industry members with participating academic centers in a new site of the existing Center for Hybrid Multicore Productivity Research (CHMPR) I/UCRC, at NC State University (NCSU) in Raleigh, NC, with the goal of conducting trans-disciplinary translational science and research of enabling better decision making in presence of Big Data. The fundamental research done at CHMPR/NCSU will be translated into technology developments, delivering practical solutions to hard problems. This translation will apply to federal agencies, government organizations, and industry sectors struggling with hard Big-Data problems, thus contributing to the progress of science and advancing the future Big-Data needs of the industry and society. Success in solving the problem of extracting value from massive data hinges on balancing fundamental research, technological know-how, and commercial market intelligence. We propose both to address recognized aspects of this problem and to develop the science and training that will address future aspects of this problem. The CHMPR/NCSU effort toward end-to-end enablement of data will focus on developing technologies and tools for bridging the time gap between the acquisition of data and real-time and long-term decision making. CHMPR/NCSU research projects will be addressing the issue of what are appropriate technologies for: storing and cleaning the data; modeling the data; making the data secure; asking the right analysis questions; pre- and post-processing the data with regard to the analysis; and, finally, making the analysis results useful. The resulting techniques will be applicable across multiple industry sectors, including national security, health care, manufacturing, energy, and business intelligence. The research being done at NCSU will contribute to the computationally intensive analytics-research program of CHMPR, while complementing its portfolio by the NCSU big-data transformation and analysis expertise. The technologies and tools resulting from the proposed work will change the results that can be obtained from the data, as opposed to just obtaining the results faster. The ultimate success of this paradigm shift by the Big-Data-analysis industry will rest on the ability of universities such as NCSU to prepare experts in taking advantage of the data-enablement science and technologies to solve a variety of real-life applications.

几乎每个研究领域和行业部门都在努力从通常被称为大数据的海量数据集中管理和提取有用的信息。建模、数据管理和分析是理解和使用这些数据集的关键要素，它们在学术界和工业界的重要性预计将呈指数级增长。一个核心挑战可以表达为：给定特定的分析任务和对可用大数据的特定限制，我们如何从数据中实现高效，高效和安全的端到端价值提取？为了应对这一挑战，我们建议在北卡罗来纳州罗利的北卡罗来纳州立大学（NCSU）现有的混合多核生产力研究中心（CHMPR） I/UCRC的新址与参与学术中心的行业成员一起开展跨学科转化科学和研究，以实现在大数据存在下更好的决策。在CHMPR/NCSU进行的基础研究将转化为技术开发，为困难问题提供实用的解决方案。该翻译将适用于与大数据难题作斗争的联邦机构、政府组织和行业部门，从而促进科学的进步，推动行业和社会对未来大数据的需求。成功解决从海量数据中提取价值的问题取决于平衡基础研究、技术诀窍和商业市场情报。我们建议既要解决这一问题的公认方面，又要发展科学和培训，以解决这一问题的未来方面。CHMPR/NCSU在数据端到端实现方面的努力将侧重于开发技术和工具，以缩短数据获取与实时和长期决策之间的时间差距。CHMPR/NCSU研究项目将解决以下问题：存储和清理数据的适当技术；数据建模；确保数据安全；提出正确的分析问题；对所述分析的数据进行预处理和后处理；最后，使分析结果有用。由此产生的技术将适用于多个行业部门，包括国家安全、医疗保健、制造业、能源和商业智能。在NCSU进行的研究将有助于CHMPR的计算密集型分析研究项目，同时补充NCSU大数据转换和分析专业知识的投资组合。从建议的工作中产生的技术和工具将改变可以从数据中获得的结果，而不仅仅是更快地获得结果。大数据分析行业模式转变的最终成功将取决于NCSU等大学培养专家利用数据支持科学技术来解决各种现实应用的能力。