A Brief Review on Leading Big Data Models

dc.contributor.author Tim, U. Sunday
dc.contributor.author Sharma, Sugam
dc.contributor.author Sharma, Sugam
dc.contributor.author Tim, Udoyara
dc.contributor.author Wong, Johnny
dc.contributor.author Gadia, Shashi
dc.contributor.author Sharma, Subhash
dc.contributor.author Wong, Johnny
dc.contributor.department Computer Science
dc.contributor.department Agricultural and Biosystems Engineering
dc.date 2018-02-17T23:41:33.000
dc.date.accessioned 2020-06-29T22:42:32Z
dc.date.available 2020-06-29T22:42:32Z
dc.date.copyright Wed Jan 01 00:00:00 UTC 2014
dc.date.issued 2014-01-01
dc.description.abstract <p>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.”</p>
dc.description.comments <p>This article is from Data Science Journal. 13, pp.138–157. DOI: <a href="http://doi.org/10.2481/dsj.14-041" target="_blank">http://doi.org/10.2481/dsj.14-041</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/abe_eng_pubs/771/
dc.identifier.articleid 2055
dc.identifier.contextkey 9277527
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath abe_eng_pubs/771
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/1571
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/abe_eng_pubs/771/2014_Sharma_BriefReview.pdf|||Sat Jan 15 01:53:01 UTC 2022
dc.source.uri 10.2481/dsj.14-041
dc.subject.disciplines Agriculture
dc.subject.disciplines Bioresource and Agricultural Engineering
dc.subject.disciplines Computer Sciences
dc.subject.disciplines Statistics and Probability
dc.title A Brief Review on Leading Big Data Models
dc.type article
dc.type.genre article
dspace.entity.type Publication
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