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Exploring Big Data Analysis: Fundamental Scientific Problems

Exploring Big Data Analysis: Fundamental Scientific Problems Although Big Data has been one of most popular topics since last several years, how to effectively conduct Big Data analysis is a big challenge for every field. This paper tries to address some fundamental scientific problems in Big Data analysis, such as opportunities, challenges, and difficulties encountered in the analysis. The challenges rise from multiple domains that include how Management Science influences data acquisition and data management, Information Science for data access and processing, Mathematics and Statistics for data understanding and Engineering for data applications. The paper outlines six open research problems on Big Data. It also reports some advances on current Big Data research, particularly in high-dimensional data and non-structured data processing. Finally, remarks on how to develop a Big Data algorithm are provided. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Data Science Springer Journals

Exploring Big Data Analysis: Fundamental Scientific Problems

Xu, Zongben; Shi, Yong
Annals of Data Science , Volume 2 (4) – Jan 9, 2016
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10 pages

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Publisher
Springer Journals
Copyright
Copyright © 2016 by Springer-Verlag Berlin Heidelberg
Subject
Business and Management; Business and Management, general; Statistics for Business/Economics/Mathematical Finance/Insurance; Computing Methodologies
ISSN
2198-5804
eISSN
2198-5812
DOI
10.1007/s40745-015-0063-7
Publisher site
See Article on Publisher Site

Abstract

Although Big Data has been one of most popular topics since last several years, how to effectively conduct Big Data analysis is a big challenge for every field. This paper tries to address some fundamental scientific problems in Big Data analysis, such as opportunities, challenges, and difficulties encountered in the analysis. The challenges rise from multiple domains that include how Management Science influences data acquisition and data management, Information Science for data access and processing, Mathematics and Statistics for data understanding and Engineering for data applications. The paper outlines six open research problems on Big Data. It also reports some advances on current Big Data research, particularly in high-dimensional data and non-structured data processing. Finally, remarks on how to develop a Big Data algorithm are provided.

Journal

Annals of Data Science – Springer Journals

Published: Jan 9, 2016

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