Big Data, Cloud Computing, and IoT (BCI) Amalgamation Model: The Art of “Reinventing Yourself” to Analysis the World in Which We Live
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The spread of omnipresent sensing technology brings with it an increasing number of innovative models. The smart mobility initiatives offer new opportunities for Intelligent Systems to maximize the utilization of real-time data that are streaming out of different sensory resources. In recent years, the convergence trend of Big Data, Cloud and IoT has received considerable attention in industry and academia. A huge amount of data is generated every day from information systems and modern digital technologies such as the Internet of things (IoT) and cloud computing. The analysis of these massive data requires a lot of effort at multiple levels to extract knowledge to facilitate decision-making. Big data analysis is therefore a topical area of research and development. The main objective of this survey is to propose Big Data, Cloud Computing, and IoT (BCI) Amalgamation Model. Additionally, this paper explores the big data characteristics, challenges, analysis techniques, and various tools associated with it. The recommendation of the suitable analysis techniques of big data that could reduce the time and increase efficiency is discussed.
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References
-
Katal, A. Wazid, M. Goudar, R.H "Big data: Issues, challenges, tools and Good practices, IEEE" Contemporary Computing (IC3), 2013 Sixth International Conference, pp:404 - 409.
Google Scholar
1
-
Shuhui Jiang, Xueming Qian, Tao Mei, Yun Fu, Personalized Travel Sequence recommendation on Multisource Big Social Media, 2016, IEEE Transactions on Big Data, Vol.2, Issue:1
Google Scholar
2
-
Gantz J, Reinsel D, Extracting value from chaos.IDC iView, 2011, pp 1?12
Google Scholar
3
-
Mayer-Schonberger V, Cukier K, Big data: a revolution that will transform how we live, work, and think. Boston: Houghton Mifflin Harcourt; 2013.
Google Scholar
4
-
Kitchin R. The real-time city? Big data and smart urbanism. Geo J. 2014, 79(1), pp: 1?14.
Google Scholar
5
-
Chen, C.P., Zhang, C.Y.: Data-intensive applications, challenges, techniques, and technologies: A survey on big data. Information Conference on. pp. 404-409. IEEE (2013)
Google Scholar
6
-
Kaiser, S., Armour, F., Espinosa, J.A., Money, W.: Big data: Issues and challenges moving forward. In: System Sciences (HICSS), 2013 46th Hawaii International Conference on. pp. 995-1004. IEEE (2013).
Google Scholar
7
-
Che, D., Safran, M., Peng, Z.: From big data to big data mining: challenges, issues, and opportunities. In: Database Systems for Advanced Applications. pp. 1-15. Springer (2013).
Google Scholar
8
-
Kata!, A., Wazid, M., Goudar, R.: Big data: Issues, challenges, tools, and good practices. In: Contemporary Computing (IC3), 2013 Sixth International Conference on. pp. 404-409. IEEE (2013).
Google Scholar
9
-
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H.: Big data: The next frontier for innovation, competition, and productivity (2011).
Google Scholar
10
-
Kim, G. H., Trim, S., & Chung, J. H. Big-data applications in the government sector. Communications of the ACM, 57(3) 78-85 (2014).
Google Scholar
11
-
Stonebraker, M., and J. Hong. Researchers 'Big Data Crisis; Understanding Design and Functionality, Communications of the ACM, 55(2), 10-11 (2012).
Google Scholar
12
-
Wu, X., Zhu, X., Wu, G.Q., Ding, W.: Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97-107 (2014).
Google Scholar
13
-
Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. In: Proceedings of the 2nd USENIX conference on Hot topics in cloud computing. vol. 10, p. 10 (2010).
Google Scholar
14
-
0. Driscoll, A., Daugelaite, J., Sleator, R.D. big data, Hadoop and cloud computing in genomics. Journal of biomedical informatics 46(5), 774-781 (2013).
Google Scholar
15
-
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, The rise of "big data" on cloud computing: review and open research issues. Information Systems, 4 7, 98-115 (2015).
Google Scholar
16
-
Simoff, S., Bohlen, M.H., Mazeika, A.: Visual data mining: theory, techniques, and tools for visual analytics, vol. 4404. Springer Science & Business Media (2008).
Google Scholar
17
-
Sawant, N., & Shah, H. Big Data Visualization Patterns. In Big Data Application Architecture Q & A 79-90 (2013).
Google Scholar
18
-
Katrina Sin and Loganathan Muthu, Applications of big data in education data mining and learning analytics ? A literature Review, ICTACT Journal on soft computing special issue on soft computing models for big data, July 2015, Vol:05, Iss: 04, pp: 1035-1049
Google Scholar
19
-
Cheikh Kacfah Emani, Nadine Cullot, Christophe Nicolle, Understandable Big Data: A Survey, Computer Science Review, 2015, Vol: 17, pp: 71-80
Google Scholar
20
-
K. Krishnan, Data warehousing in the age of big data, in The Morgan Kaufmann Series on Business Intelligence, Elsevier Science, 2013.
Google Scholar
21
-
H.V. Jagadish, D. Agarwal, P.Bernstein, Challenges, and Opportunities in Big Data, The Community Research Association, 2015
Google Scholar
22
-
K. Davis, D. Patterson, ?Ethics of Big Data: Balancing Risk and innovation?, O?Reilly Media, 2012.
Google Scholar
23
-
Adil Fahad, Najlaa Alshatri, Zahir Tari, Abdullah Alamri, Ibrahim Khalil, Albert Y. Zomaya, Sebti Foufo Abdelaziz Bouras, A Survey of Clustering Algorithms for Big Data Taxonomy and Empirical Analysis, on Emerging Topics on Computing, IEEE, 11 June 2014.
Google Scholar
24
-
G. Ingersoll, Introducing apache mahout: Scalable, commercial friendly machine learning for building intelligent applications, White Paper, IBM Developer Works, 2009, pp. 1-18.
Google Scholar
25
-
Mike Barlow, Real-Time Big Data Analytics: Emerging Architecture, ISBN: 978-1-449-36421-2, 2013
Google Scholar
26
-
MH. Kuo, T. Sahama, A. W. Kushniruk, E. M. Borycki, and D. K. Grunwell, Health big data analytics: current perspectives, challenges, and potential solutions, International Journal of Big Data Intelligence, 1 (2014), pp.114-126.
Google Scholar
27
-
R. Nambiar, A. Sethi, R. Bhardwaj, and R. Varghese, A look at challenges and opportunities of big data analytics in healthcare, IEEE International Conference on Big Data, 2013, pp.17-22.
Google Scholar
28
-
A. Azzini and P. Ceravolo, "Consistent process mining over big data triple stores," in 2013 IEEE International Congress on Big Data. IEEE, 2013, pp. 54-61.
Google Scholar
29
-
C. Ardagna, R. Asal, E. Damiani, and Q. Vu, "From security to assurance in the cloud: A survey," ACM Computing Surveys (CSUR), vol. 48, no. 1, pp. 2:1-2:50, August 2015.
Google Scholar
30
-
E. Gasiorowski-Denis, Big plans for big data, March 2014, http://www.iso.org/iso/home/news~index/news_archive/news.htm? refid=Refl821.
Google Scholar
31
-
TUT-T, Big data - Cloud computing based requirements and capabilities, November 2015, http://www.itu.int/rec/T-REC-Y. 3600-201511-T.
Google Scholar
32
-
ISO/IEC, !SOI/EC CD 20546: Information Technology - Big Data - Definition and Vocabulary, 2016, http://www.iso.org/iso/home/store/ catalogue_tc/catalogue_detail.htm?csnumber=68305.
Google Scholar
33
-
V. Markl, ?Breaking the chains: On declarative data analysis and data independence in the big data era,? Proc. of VLDB Endowment, vol. 7, no. 13, pp. 1730?1733, August 2014.
Google Scholar
34