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Author: Sherry L. Xie

Affiliation: School of Information Resource Management, Renmin University of China, 59 Zhongguancun, Beijing and China

ISBN: 978-989-758-330-8

Keyword(s): Big Data, Data Feature, Existing Data, To-be-collected Data, Data Science, Data Analytics.

Abstract: This paper reports on a study that aimed to examine the term big data for its meaning in a particular setting. The study chose the U.S. Federal Government as its case and analysed all the big data projects and programs identified as representative of the U.S. Big Data Initiative. It constructed an analytical framework and generated findings in forms of statistic descriptions and narrative discussions. The study discovered that 1) not all the big data projects and programs possess in a collective manner the typical 3 Vs (i.e., volume, variety, and velocity), 2) variety appears to be the most valued characteristic, and 3) to-be-collected data lags largely behind existing data, indicating that technologies such as the Internet of Things are still at the stage of being developed. It also unrevealed that the U.S. Federal Government’s current big data focus is heavily placed on IT and the term big data has made that focus hidden. It then suggests to sufficiently distinguish data and the tec hnologies underlying the various features of data so that collaborations between the owners of data and technologies can be forged with easiness and big data benefits can be realized with efficiency and effectiveness. (More)

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Paper citation in several formats:
Xie, S. (2018). Looking into Big Data: The Case of the U. S. Federal Government.In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS, ISBN 978-989-758-330-8, pages 144-152. DOI: 10.5220/0006919001440152

@conference{kmis18,
author={Sherry L. Xie.},
title={Looking into Big Data: The Case of the U. S. Federal Government},
booktitle={Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS,},
year={2018},
pages={144-152},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006919001440152},
isbn={978-989-758-330-8},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS,
TI - Looking into Big Data: The Case of the U. S. Federal Government
SN - 978-989-758-330-8
AU - Xie, S.
PY - 2018
SP - 144
EP - 152
DO - 10.5220/0006919001440152

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