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Forecasting Innovation Pathways of Big Data & Analytics

Forecasting Innovation Pathways of Big Data & Analytics
预测大数据创新路径
批准号:
1527370
负责人:
Alan Porter
金额:
$5.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2015
资助国家:
美国
项目状态:
已结题
起止时间:
2015-04-01 至 2017-03-31

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中文摘要
翻译
研究与开发(R&D)导致了新的基于科学的技术创新的创造。有必要提高对研究发现如何转化为新技术,然后发展为有用创新的理解。用于解决这一问题的方法要么利用大量历史信息中的趋势,要么利用专家的判断。该项目将应用先进的研究技术,加强识别趋势和模式的方法,并帮助预测创新途径。通过明智地投资于前景光明的研发,这些知识对于促进科学进步至关重要。它还可以帮助技术管理,以确定如何最好地推进一个特定的科学领域。该项目将提供一个案例研究,以改进被认为对改进预测创新途径的方法至关重要的五个分析过程。要分析的案例是什么?大数据分析?? 一个具有重大国家意义的话题。弄清楚如何从大数据集中获得优势将影响国家的科学进步、工业生产力和国防。与此同时,大数据也带来了隐私和安全等问题。之所以选择大数据这个主题,是因为美国政府问责局(GAO)正在对其进行研究。我们期望与GAO分享方法和发现的信息,以深入了解如何使我们的方法更有用。该提案扩展了先前资助的SciSIP项目(1064146),以解决解决GAO案例的时间有限的机会。
英文摘要
Research and development (R&D) leads to the creation of new science-based technology innovations. There is a need to improve understanding of how research discoveries translate into new technologies and then develop into useful innovation. Methods used to address this concern either draw on trends in large amounts of historic information or draw on expert judgment. This project will apply advanced research techniques to enhance methods to identify trends and patterns, and to help forecast innovation pathways. Such knowledge is vital to promote scientific progress by investing judiciously in high promise R&D. It also can aid in technology management to determine how best to advance a specific field of science.This project will provide a case study to improve five analytical processes, identified as vital to improve the methodology of forecasting innovation pathways. The case to be analyzed is ?big data & analytics? ? a topic of great national importance. Figuring out how to gain advantage from large data sets will impact national scientific progress, industrial productivity, and defense. At the same time, big data poses issues of privacy and security, among others. The topic of big data was selected because it is under study by the U.S. Government Accountability Office (GAO). We anticipate sharing information on methods and findings with GAO to gain insight into ways to make our methodology more useful. . This proposal extends a previously funded SciSIP project (1064146) to address a time-limited opportunity to address the GAO case.
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