NIST Big Data Interoperability Framework: Volume 2, Big Data Taxonomies

Abstract

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. AbstractBig 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.

DaLiF: a data lifecycle framework for data-driven governments

“…Furthermore, we found certain data lifecycle studies focusing on specific big data aspects. Such data aspects include data quality [17,55], data processing [28], data conception [89,90], data management [55], security [88], data strategy [30,66], analytic [91], feedback and data refinement [92,93]. In the literature, the various data lifecycles exhibit many differences particularly about the objectives, intended audience, phases, actors, and attention [94].…”

“…USA (NIST)-big data lifecycle model This data lifecycle model is introduced by the National Institute of Standards and Technology-NIST, Deptt: of Commerce, the USA in 2015. The lifecycle consists of the following phases, collection, preparation, analysis, and action (analytics, visualization, access) [91]. This model concentrates more on the data analytics part compared to data planning, data filtering, and data enrichment.…”

“…Preparation The preparation phase refers to data integration, filtering, and enrichment [91,127,135]. The integration consolidates all these data silos into a single place with a coherent and homogeneous structure [26,46,155].…”

“…[152]. The analysis phase includes analysis of unstructured data [91]. The output of the data analysis phase includes knowledge, the discovery of new insights, new data, interpretations and/or new datasets [42,54,61,165].…”

“…• Data sources selection like identification of descriptions, data sources location, file types, and data provenance [58,91]. • Perform analysis of data to extract knowledge and discover new insights, and then decision-makers use this knowledge intelligently to generate value for public organizations [17, 42-44, 54, 61].…”