Agricultural and Biosystems Engineering Publications
Campus Units
Agricultural and Biosystems Engineering, Computer Science
Document Type
Article
Publication Version
Published Version
Publication Date
2014
Journal or Book Title
Data Science Journal
Volume
13
First Page
138
Last Page
157
DOI
10.2481/dsj.14-041
Abstract
Today, science is passing through an era of transformation, where the inundation of data, dubbed data deluge is influencing the decision making process. The science is driven by the data and is being termed as data science. In this internet age, the volume of the data has grown up to petabytes, and this large, complex, structured or unstructured, and heterogeneous data in the form of “Big Data” has gained significant attention. The rapid pace of data growth through various disparate sources, especially social media such as Facebook, has seriously challenged the data analytic capabilities of traditional relational databases. The velocity of the expansion of the amount of data gives rise to a complete paradigm shift in how new age data is processed. Confidence in the data engineering of the existing data processing systems is gradually fading whereas the capabilities of the new techniques for capturing, storing, visualizing, and analyzing data are evolving. In this review paper, we discuss some of the modern Big Data models that are leading contributors in the NoSQL era and claim to address Big Data challenges in reliable and efficient ways. Also, we take the potential of Big Data into consideration and try to reshape the original operationaloriented definition of “Big Science” (Furner, 2003) into a new data-driven definition and rephrase it as “The science that deals with Big Data is Big Science.”
Access
Open
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Copyright Owner
The authors
Copyright Date
2014
Language
en
File Format
application/pdf
Recommended Citation
Sharma, Sugam; Tim, Udoyara S.; Wong, Johnny S.; Gadia, Shashi; and Sharma, Subhash, "A Brief Review on Leading Big Data Models" (2014). Agricultural and Biosystems Engineering Publications. 771.
https://lib.dr.iastate.edu/abe_eng_pubs/771
Included in
Agriculture Commons, Bioresource and Agricultural Engineering Commons, Computer Sciences Commons, Statistics and Probability Commons
Comments
This article is from Data Science Journal. 13, pp.138–157. DOI: http://doi.org/10.2481/dsj.14-041. Posted with permission.