The V‟s as a basis of Big Data & Data Intensive Science Discoveries

  • Anshuman Dwivedi
Keywords: Big Data; Data-Intensive Scientific Discovery; Volume; Velocity; Variety; Veracity; Value; Viability; Validity; Volatility; Variability; Visualisation


This survey attempts to consolidate the hitherto fragmented discussions on big data and its harness potential to extract the knowledge. Firstly, various definitions and the features of Big Data are analyzed. Secondly, we have surveyed the several existing and new aspects of the data-intensive scientific discovery in terms of Vs and concluded that the systematic treatment of Vs can convert “data-centric organizational” into "knowledge-centric organizational”. At last, based on the various research papers available, we have derived a probable big data dimensions’ model as 6V-6O.


[1] Jeremy Ginsberg1 , Matthew H. Mohebbi1 , Rajan S. Patel1 , Lynnette Brammer2 , Mark S. Smolinski1, and Larry Brilliant, “Detecting influenza epidemics using search engine query data”, Nature, February 2009, See: doi:10.1038/nature07634.
[2] Gordon Bell, Tony Hey, Alex Szalay, “Beyond the data deluge”, Science, 2009.
[3] Tony Hey, Stewart Tansley, Kristin Tolle, “The fourth paradigm: data-intensive scientific discovery”, Microsoft Research, 2009.
[4] Tim Harford, “Big data: are we making a big mistake?”, Finiancial Times, March 28-2014, See:
[5] Radu Tudoran., “High-Performance Big Data Management Across Cloud Data Centers”, Phd Thesis, Radu-Marius Tudoran (ENS Rennes - IRISA / KerData), Computer Science, page 24, Jan 2015.
[6] Edd Dumbill, “Defining Big Data”. May 2014, Forbes,
[7] J. Gantz, “Extracting value from chaos”, IDC research report IDC research report, page 6, 2011.
[8] J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh and A. Byers, “Big data: The next frontier for innovation, competition, and productivity”, 2011.
[9] Michael Cox, David Ellsworth, “Application-controlled demand paging for out-of-core visualization”, Proceedings of the IEEE 8th conference on Visualization, 1997.
[10] Gil Press, A Very Short History Of Big Data, Forbes, May 2013,
[11] John R. Mashey, "Big data ... And the next wave of infrastress", Slides from invited talk. Usenix, 25 April 1998.
[12] Pettey & Goasduff, "The three Vs", Gartner, 2011.
[13] Sulayman K. Sowe, Koji Zettsu, Curating, “Big data made simple: perspectives from scientific communities”, Big Data. Mar 2014.
[14] K. Davis, D. Patterson, “Ethics of big data: balancing risk and innovation”, O‟Reilly Media, 2012.
[15] Qiang Yang, “Introduction to the IEEE Transactions on Big Data”, Jan 2015.
[16] Oracle White Paper, “Information management and big data : a reference architecture”, September 2014,
[17] Peer Research Big Data Analytics Intel‟s IT Manager Survey, “How organizations are using big data”, August 2012,
How to Cite
Dwivedi, A. (2018, April 15). The V‟s as a basis of Big Data & Data Intensive Science Discoveries. ASIAN JOURNAL FOR CONVERGENCE IN TECHNOLOGY (AJCT ) -UGC LISTED, 4(I). Retrieved from
Computer Science and Engineering