Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/4550
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dc.contributor.authorBhadoria, Robin Singhen_US
dc.date.accessioned2022-03-17T01:00:00Z-
dc.date.accessioned2022-03-17T15:34:49Z-
dc.date.available2022-03-17T01:00:00Z-
dc.date.available2022-03-17T15:34:49Z-
dc.date.issued2016-
dc.identifier.citationJadon, K. S., & Yadav, R. (2016). Analysis of costing issues in big data. The human element of big data: Issues, analytics, and performance (pp. 63-78) doi:10.1201/9781315368061en_US
dc.identifier.isbn9781498754187; 9781498754156-
dc.identifier.otherEID(2-s2.0-85052957120)-
dc.identifier.urihttps://doi.org/10.1201/9781315368061-
dc.identifier.urihttps://dspace.iiti.ac.in/handle/123456789/4550-
dc.description.abstractThe term Big Data was coined because of the huge data sets available with a large disparate complex structure for processing, analyzing, and recognizing for further results or processes. Big Data analytics deals with research into massive amounts of data to reveal hidden information and secret correlations. With the help of these useful and deeper insights into the data, companies and organizations can overpower their competitors. This is the reason why the research in this field is gaining interest day by day and therefore it needs to be carried out more carefully and accurately. This chapter deals with the architectural perspective for Big Data, content of Big Data samples, methods, scopes, advantages, and challenges. © 2017 by Taylor & Francis Group, LLC.en_US
dc.language.isoenen_US
dc.publisherCRC Pressen_US
dc.sourceThe Human Element of Big Data: Issues, Analytics, and Performanceen_US
dc.subjectArchitectural analysisen_US
dc.subjectBig Data Analyticsen_US
dc.subjectComplex structureen_US
dc.subjectHidden informationen_US
dc.subjectBig dataen_US
dc.titleSupportive architectural analysis for big dataen_US
dc.typeBook Chapteren_US
Appears in Collections:Department of Computer Science and Engineering

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