A Review Paper on Architecture of Big Data
AUTHORS
SangramKeshari Swain,Associate Professor, Department of Computer Science & Engineering, School of Engineering & Technology, Bhubaneswar Campus Centurion University of Technology and Management, Odisha, India
Srinivas Prasad,Centurion University of Technology and Management, Odisha, India Professor, Department of Computer Science & Engineering, GMRIT, Rajam, Andhra Pradesh, India.
ManasRanjan Senapati,Associate Professor, Department of Information Technology, Veer SurendraSai University of Technology, Burla, Odisha, India
ABSTRACT
Every moment data on the internet has been developing at an exponential pace.The greatest challenge is to store, process and analyze this large amount of data in such a way that it is less time-consuming.Big Data are more focus on both the playing areas, i.e. in industry and in skill. The technology is shifting tofunctional models and data centric architecture. It is essentially needed to determine the elements of the architecture, operational models, and basic information models that formed Big Data Ecosystem. This paper focuses about the Big Data Architecture Framework to explain all looks of the Big Data Ecosystem. This will include the components like Big Data Architecture Framework, Big Data Infrastructure, Big Data Definitions and Analytics, Big Data structures, Big Data Lifecycle Management, and models.
KEYWORDS
Big Data Infrastructure (BDI), Big Data Architecture Framework (BDAF), Big Data Lifecycle Management (BDLM)
REFERENCES
[1] “Defining Architecture Components of the Big Data Ecosystem”.https://pdfs.semanticscholar.org/4995/8723525ee987d11e27cf169183b79b2c6ec5.pdf.
[2] Riding the wave: How Europe can gain from the rising tide of scientific data. Final report of the High-Level Expert Group on Scientific Data. [Online]. Available at http://cordis.europa.eu/fp7/ict/e-infrastructure/docs/hlg-sdi-report.pdf.October (2010).
[3] “Defining Big Data Architecture Framework”: Outcome of the Brainstorming Session at the University of Amsterdam,Presented at NBD-WG, 24 July 2013 [Online]. Available: http://bigdatawg.nist.gov/_uploadfiles/M0055_v1_7606723276.pdf, 17 July (2013).(CrossRef)(Google Scholar)
[4] “Architecture Framework and Components for the Big Data Ecosystem”. Available: http://docplayer.net/1631822-Architecture-framework-and-components-for-the-big-data-ecosystem.html DOI:10.1109/CTS.2014.6867550 .
[5] “Big Data definition”, Gartner, Inc. [Online]. Availablehttp://www.gartner.com/it-glossary/big-data/.
[6] S.Sicular, “Gartner's Big Data Definition Consists of Three Parts, Not to Be Confused with Three "V"s”, Gartner, Inc. [Online]. Available: http://www.forbes.com/sites/gartnergroup/2013/03/27/gartners-big-data-definition-consists -of-three-parts-not-to-be-confused-with-three-vs/, . 27 March (2013).
[7] Porter M.F, Snowball: “A language for stemming algorithms”. (2001).
[8] M.Dunja, “Automatic word lemmatization”. Proceedings B of the 5th International Multi- Conference Information Society IS. 2002, 153-159.
[9] N. Zhong, Y. F. Li, and S. T. Wu, “Effective Pattern Discovery for Text Mining”, IEEE Transactions on Knowledge And Data Engineering, vol. 24, no.1, January (2012).(CrossRef)(Google Scholar)
[10] Y. F. Li, S. T.Wu and X. H. Tao, “Effective Pattern Taxonomy Mining in Text Documents”, ACM, October 26-30, (2008). (CrossRef)(Google Scholar)
[11] P. Kalesha, M. B. Rao and Ch. Kavitha, “Efficient Preprocessing and Patterns Identification Approach for Text Mining”, International Journal of Computer Trends and Technology (IJCTT), vol. 6, no. 2, December (2013).
[12] Z. Elberrichi, A. Rahmoun and M. Bentaalah, “Using WordNet for Text Categorization”, International Arab Journal of Information Technology, vol. 5, no. 1, January (2008).