Journal of Computer Sciences and Applications
ISSN (Print): 2328-7268 ISSN (Online): 2328-725X Website: http://www.sciepub.com/journal/jcsa Editor-in-chief: Minhua Ma, Patricia Goncalves
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Journal of Computer Sciences and Applications. 2015, 3(6), 137-142
DOI: 10.12691/jcsa-3-6-6
Open AccessArticle

Future Trends in Cloud Computing and Big Data

Smaranika Mohapatra1, , Jharana Paikaray1 and Neelamani Samal1

1Department of Computer Science & Engg, Gandhi Institute for Education & Technology, Bhubaneswar-752060, Odisha, India

Pub. Date: December 30, 2015

Cite this paper:
Smaranika Mohapatra, Jharana Paikaray and Neelamani Samal. Future Trends in Cloud Computing and Big Data. Journal of Computer Sciences and Applications. 2015; 3(6):137-142. doi: 10.12691/jcsa-3-6-6

Abstract

In recent years, accompanied by lower prices of information and communications technology (ICT) equipment and networks, various items of data gleaned from the real world have come to be accumulated in cloud data centers. There are increasing hopes that analysis of this massive amount of data will provide insight that is valuable to both businesses and society. Since tens of terabytes (TBs) or tens of petabytes (PBs) of data, big data, should be handled to make full use of it, there needs to be a new type of technology different from ordinary ICT. It revolves around different areas of analytics and Big Data. Through a detailed survey, we identify possible gaps in technology and provide recommendations for the research community on future directions on Cloud-supported Big Data computing and analytics solutions.

Keywords:
big data cloud computing analytics data management

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]  X. Sun, B. Gao, Y. Zhang, W. An, H. Cao, C. Guo, W. Sun, Towards delivering analytical solutions in cloud: Business models and technical challenges, in: Proceedings of the IEEE 8th International Conference on e-Business Engineering (ICEBE 2011), IEEE Computer Society, Washington, USA, 2011, pp. 347-351.
 
[2]  P.R. Krishna, K.I. Varma, Cloud Analytics: A Path Towards Next Generation Affordable BI, White paper, Infosys (2012).
 
[3]  P.S. Yu, On mining big data, in: J. Wang, H. Xiong, Y. Ishikawa, J. Xu, J. Zhou (Eds.), Web-AgeInformation Management, in: Lecture Notes in Computer Science, vol. 7923, Springer-Verlag, Berlin, Heidelberg, 2013, p. XIV.
 
[4]  P. Russom, Big Data Analytics, TDWI best practices report, The Data Warehousing Institute (TDWI) Research (2011).
 
[5]  X. Sun, B. Gao, Y. Zhang, W. An, H. Cao, C. Guo, W. Sun, Towards delivering analytical solutions in cloud: Business models and technical challenges, in: Proceedings of the IEEE 8th International Conference on e-Business Engineering (ICEBE 2011), IEEE Computer Society, Washington, USA, 2011, pp. 347-351.
 
[6]  R. Bonney, J.L. Shirk, T.B. Phillips, A. Wiggins, H.L. Ballard, A.J. Miller-Rushing, J.K. Parrish, Next steps for citizen science, Science 343 (2014) 1436-1437.
 
[7]  X. Zhang, E. Zhang, B. Song, F. Wei, Towards Building an Integrated Information Platform for Eco-city, in: Proceedings of the 7th International Conference on e Business Engineering (ICEBE 2010), 2010, pp. 393-398.
 
[8]  S. Ghemawat, H. Gobioff, S.-T. Leung, The google file system, in: Proceedings of the 9th ACM Symposium on Operating Systems Principles (SOSP 2003), ACM, New York, USA, 2003, pp. 29-43.
 
[9]  J. Cohen, B. Dolan, M. Dunlap, J.M. Hellerstein, C. Welton, MAD skills: new analysis practices for big data, Proceedings of the VLDB Endow 2 (2) (2009) 1481-1492.
 
[10]  Amazon redshift, http://aws.amazon.com/redshift/.
 
[11]  J. Han, H. E, G. Le, J. Du, Survey on NoSQL database, in: 6th International Conference on Pervasive http://media.amazonwebservices.com/AWS_Amazon_E R_Best_Practices. pdf.
 
[12]  J. Dean, S. Ghemawat, MapReduce: Simplified Data Processing on Large Clusters, Communications of the ACM 51(1).
 
[13]  Apache Hadoop, http://hadoop.apache.org.
 
[14]  H. Herodotou, H. Lim, G. Luo, N. Borisov, L. Dong, F.B. Cetin, S. Babu, Starfish: A Self-tuning System for Big Data Analytics, in: Proceedings of the 5th Biennial Conference on Innovative Data Systems Research (CIDR 2011), 2011, pp. 261-272.
 
[15]  A. Balmin, K. Beyer, V. Ercegovac, J.M.F. Ozcan, H. Pirahesh, E. Shekita, Y. Sismanis, S. Tata, Y. Tian, A platform for eXtreme Analytics, IBM J. Res. Dev. 57 (3-4) (2013) 4:1-4:11.
 
[16]  Zementis – adaptive decision technology, http://www.zementis.com (2012).
 
[17]  IBM SmartCloud Enterprise, http://www-935.ibm.com/services/us/en/ cloud-enterprise/ (2012).
 
[18]  Google Prediction API, https://developers.google.com/prediction/.
 
[19]  P. Deyhim, Best practices for Amazon EMR, White paper, Amazon (2013). URL http://media.amazonwebservices.com/AWS_Amazon_EMR_Best_Practices. pdf.
 
[20]  F.B. Viegas, M. Wattenberg, F. van Ham, J. Kriss, M. McKeon, ManyEyes: a Site for Visualization at Internet Scale, IEEE Trans. Vis. Comput. Graphics 13 (6) (2007) 1121-1128.
 
[21]  SAP Crystal Solutions,http://www.crystalreports.com/.
 
[22]  panXpan, https://www.panxpan.com.
 
[23]  FusionChars, http://www.fusioncharts.com/.
 
[24]  Cloud9 Analytics, http://www.cloud9analytics.com.
 
[25]  D. Lazer, R. Kennedy, G. King, A. Vespignani, The Parable of google flu: Traps in big data analysis, Science 343 (2014) 1203-1205.
 
[26]  P. Deyhim, Best practices for Amazon EMR, Whitepaper , Amazon (2013). URL。
 
[27]  A. McAfee, E. Brynjolfsson, Big data: The management revolution, Harv. Bus. Rev. (2012) 60-68.