Back
 JCC  Vol.5 No.3 , March 2017
Big Data for Organizations: A Review
Abstract:
Big data challenges current information technologies (IT landscape) while promising a more competitive and efficient contributions to business organizations. What big data can contribute to is what organizations have been wanted for a long time ago. This paper presents the nature of big data and how organizations can advance their systems with big data technologies. By improving the efficiency and effectiveness of organizations, people can benefit the can take advantages of a more convenient life contributed by Information Technology.
Cite this paper: Khine, P. , Shun, W. (2017) Big Data for Organizations: A Review. Journal of Computer and Communications, 5, 40-48. doi: 10.4236/jcc.2017.53005.
References

[1]   Manyika, J., et al. (2011) Big Data: The Next Frontier for Innovation, Competition, and Productivity. San Francisco, McKinsey Global Institute, CA, USA.

[2]   Laudon, K.C. and Laudon, J.P. (2012) Management Information Systems: Managing the Digital Firm. 13th Edition, Pearson Education, US.

[3]   House, W. (2012) Fact Sheet: Big Data across the Federal Government.

[4]   Mousanif, H., Sabah, H., Douiji, Y. and Sayad, Y.O. (2014) From Big Data to Big Projects: A Step-by-Step Roadmap. International Conference on Future Internet of Things and Cloud, 373-378

[5]   Oracle Enterprise Architecture White Paper (March 2016) An Enterprise Architect’s Guide to Big Data: Reference Architecture Overview.

[6]   Laney, D. (2001) 3D Data Management: Controlling Data Volume, Velocity and Variety, Gartner Report.

[7]   Sagiroglu, S. and Sinanc, D. (2013) Big Data: A Review. International Conference on Collaboration Technologies and Systems (CTS), 42-47.

[8]   de Roos, D., Zikopoulos, P.C., Melnyk, R.B., Brown, B. and Coss, R. (2012) Hadoop for Dummies. John Wiley & Sons, Inc., Hoboken, New Jersey, US.

[9]   Grolinger, K., Hayes, M., Higashino, W.A., L’Heureux, A., Allison, D.S. and Capretz1, M.A.M. (2014) Challenges of MapReduce in Big Data, IEEE 10th World Congress on Services, 182-189.

[10]   Hurwitz, J.S., Nugent, A., Halper, F. and Kaufman, M. (2012) Big Data for Dummies, 1st Edition, John Wiley & Sons, Inc, Hoboken, New Jersey, US.

[11]   Han, J., Kamber, M. and Pei, J. (2006) Data Mining: Concepts and Techniques. 3rd Edition, Elsevier (Singapore).

[12]   Data Lake. https://en.m.wikipedia.org/wiki/Data_lake

[13]   Hu, H., Wen, Y.G., Chua, T.-S. and Li, X.L. (2014) Toward Scalable Systems for Big Data Analytics: A Technology Tutorial. IEEE Access, 2, 652-687. https://doi.org/10.1109/ACCESS.2014.2332453

[14]   Dean, J. and Ghemawat, S. (2008) MapReduce: Simplified Data Processing on Large Clusters. Commun ACM, 107-113. https://doi.org/10.1145/1327452.1327492

[15]   Storm Project. http://storm.apache.org/releases/2.0.0-SNAPSHOT/Concepts.html

[16]   Neumeyer, L., Robbins, B., Nair, A. and Kesari, A. (2010) S4: Distributed Stream Computing Platform. 2010 IEEE International Conference on Data Mining Workshops (ICDMW). https://doi.org/10.1109/ICDMW.2010.172

 
 
Top