NIST Big Data Interoperability Framework: Volume 2, Big Data Taxonomies
NIST Big Data Interoperability Framework: Volume 2, Big Data Taxonomies
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DOI：
10.6028/nist.sp.1500-2
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发表时间：
2015-10
期刊：
影响因子：
--
通讯作者：
Wo L. Chang;N. Grady
中科院分区：
文献类型：
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作者：
Wo L. Chang;N. Grady
NIST的信息技术实验室（ITL）通过为国家的测量和标准基础设施提供技术领导力来促进美国的经济和公共福利。 ITL的生产性使用责任包括管理，行政，技术和物理标准除了联邦信息系统中与国家安全相关的信息以外。大数据，数据可以压倒传统的技术方法，数据的增长超过了数据分析的科学和技术进步，以促进大数据的进步，NIST大数据公共工作组（NBD-PWG）正在努力发展一致重要的，与大数据有关的基本概念。包含NBD-PWG开发的大数据分类法。
The Information Technology Laboratory (ITL) at NIST promotes the U.S. economy and public welfare by providing technical leadership for the Nation’s measurement and standards infrastructure. ITL develops tests, test methods, reference data, proof of concept implementations, and technical analyses to advance the development and productive use of information technology. ITL’s responsibilities include the development of management, administrative, technical, and physical standards and guidelines for the cost-effective security and privacy of other than national security-related information in federal information systems. This document reports on ITL’s research, guidance, and outreach efforts in Information Technology and its collaborative activities with industry, government, and academic organizations. Abstract Big Data is a term used to describe the large amount of data in the networked, digitized, sensor-laden, information-driven world. While opportunities exist with Big Data, the data can overwhelm traditional technical approaches and the growth of data is outpacing scientific and technological advances in data analytics. To advance progress in Big Data, the NIST Big Data Public Working Group (NBD-PWG) is working to develop consensus on important, fundamental concepts related to Big Data. The results are reported in the NIST Big Data Interoperability Framework series of volumes. This volume, Volume 2, contains the Big Data taxonomies developed by the NBD-PWG. These taxonomies organize the reference architecture components, fabrics, and other topics to lay the groundwork for discussions surrounding Big Data.