基于数据挖掘技术的科研数据管理大数据分析框架研究

M. Vaidya, M. Sanjeeva
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引用次数: 0

摘要

研究作为高等教育的重要组成部分,正在经历一场蜕变。跨学科的研究人员越来越多地利用电子工具来收集、分析和组织数据。这种“数据洪流”催生了在组织中制定政策、基础设施和服务的需求,其目的是协助研究人员创建、收集、操纵、分析、传输、存储和保存数据集。研究现在是在数字领域进行的,研究人员之间产生和交换数据。与图书馆数据相结合的科研数据管理,由于其体量大、速度快、多样性明显的特点,也可以毫无疑问地视为大数据。综上所述,可以说大数据集需要更有用、更可见、更可访问。有了新的强大的大数据分析工具,比如信息可视化工具,研究人员可以用新的方式看待数据,并从中挖掘出他们想要的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Study of Big Data Analytical Frameworks in Research Data Management Using Data Mining Techniques
Research, which is an integral part of higher education, is undergoing a metamorphosis. Researchers across disciplines are increasingly utilizing electronic tools to collect, analyze, and organize data. This “data deluge” creates a need to develop policies, infrastructures, and services in organisations, with the objective of assisting researchers in creating, collecting, manipulating, analysing, transporting, storing, and preserving datasets. Research is now conducted in the digital realm, with researchers generating and exchanging data among themselves. Research data management in context with library data could also be treated as big data without doubt due its properties of large volume, high velocity, and obvious variety. To sum up, it can be said that big datasets need to be more useful, visible, and accessible. With new and powerful analytics of big data, such as information visualization tools, researchers can look at data in new ways and mine it for information they intend to have.
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