A Review on Latest Technologies in Big Data Analysis

  • Authors

    • Castro S
    • Pushpalakshmi R
    2018-08-04
    https://doi.org/10.14419/ijet.v7i3.1.16806
  • Big data analysis, cloud computing, Internet of Things, Data Storage, and Knowledge Discovery.

  • Abstract

    In this digital world, the modern information systems have produced a large amount of data which needs huge depositary in terms of terabytes for storage. Some of the digital technologies such as cloud computing and Internet of Things (IoT) are considered as the major sources of such large data. It is necessary to extract knowledge by analyzing these huge data which needs several attempts at multiple stages for decision making. Thus, the recent researches have focused on the analysis of big data. The main aim of this paper is to investigate the challenges of big data, applications, opportunities, implantation tools and its research problems. Thus, this study presents a platform to investigate big data at various levels. Moreover, it initiates a novel perspective for researchers to provide the solutions according to the challenges and research problems.

     

  • References

    1. [1] Kryftis, Y., Mavromoustakis, C.X., Batalla, J.M., Mastorakis, G., Pallis, E., Skourletopoulos, G.: Resource usage prediction for optimal and balanced provision of multimedia services. Proceedings of the 2014 IEEE 19th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD 2014), pp. 255–259. Greece, Athens (2014)

      [2] Skourletopoulos, G., Xanthoudakis, A.: Developing a business plan for new technologies: application and implementation opportunities of the interactive digital (iDTV) and internet protocol (IPTV) television as an advertising tool. Bachelor’s Degree Dissertation, Technological Educational Institute of Crete, Greece (2012)

      [3] Mavromoustakis, C.X., Mastorakis, G., Bourdena, A., Pallis, E., Kormentzas, G., Rodrigues, J.J.P.C.: Context-oriented opportunistic cloud offload processing for energy conservation in wireless devices. Proceedings of the IEEE Global Communications Conference (GLOBECOM 2014)—The Second International Workshop on Cloud Computing Systems, Networks, and Applications (CCSNA), pp. 24–30. Austin, Texas, USA (2014a)

      [4] Mavromoustakis, C.X., Andreou, A., Mastorakis, G., Bourdena, A., Batalla, J.M., Dobre, C.: On the performance evaluation of a novel offloading-based energy conservation mechanism for wireless devices. In: Proceedings of the 6th International Conference on Mobile Networks and Management (MONAMI 2014), 22–24 Sept 2014. Wuerzburg, Germany (2014b)

      [5] Mavromoustakis, C.X., Mastorakis, G., Bourdena, A., Pallis, E., Kormentzas, G., Dimitriou, C.: Joint energy and delay-aware scheme for 5G mobile cognitive radio networks. In: Proceedings of IEEE GlobeCom 2014, Symposium on Selected Areas in Communications: GC14 SAC Green Communication Systems and Networks, Austin, TX, USA (2014c)

      [6] Amazon Web Services, Inc.: Elastic Compute Cloud (EC2). http://aws.amazon.com/ec2 (2015). Accessed 18 Oct 2015

      [7] Google, Inc.: App engine—platform as a service. https://cloud.google.com/appengine (2015). Accessed 18 Oct 2015.

      [8] IBM Corporation: IBM bluemix. http://www.ibm.com/cloud-computing/bluemix (2015b). Accessed 18 Oct 2015

      [9] Microsoft Corporation: Microsoft azure: cloud computing platform and services. https://azure. microsoft.com (2015). Accessed 18 Oct 2015

      [10] Buyya, R., Ranjan, R., Calheiros, R.N.: Intercloud: utility-oriented federation of cloud computing environments for scaling of application services. Algorithms Arch. Parallel Process. 6081, 13–31 (2010)

      [11] 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.

      [12] R. Nambiar, A. Sethi, R. Bhardwaj and R. Vargheese, A look at challenges and opportunities of big data analytics in healthcare, IEEE International Conference on Big Data, 2013, pp.17-22.

      [13] Z. Huang, A fast clustering algorithm to cluster very large categorical data sets in data mining, SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, 1997.

      [14] T. K. Das and P. M. Kumar, Big data analytics: A framework for unstructured data analysis, International Journal of Engineering and Technology, 5(1) (2013), pp.153-156.

      [15] T. K. Das, D. P. Acharjya and M. R. Patra, Opinion mining about a product by analyzing public tweets in twitter, International Conference on Computer Communication and Informatics, 2014.

      [16] L. A. Zadeh, Fuzzy sets, Information and Control, 8 (1965), pp.338- 353.

      [17] J. F.Peters, Near sets. General theory about nearness of objects, Applied Mathematical Sciences, 1(53) (2007), pp.2609-2629.

      [18] Z. Pawlak, Rough sets, International Journal of Computer Information Science, 11 (1982), pp.341-356.

      [19] D. Molodtsov, Soft set theory first results, Computers and Mathematics with Aplications, 37(4/5) (1999), pp.19-31.

      [20] I. T.Jolliffe, Principal Component Analysis, Springer, New York, 2002.

      [21] R. Wille, Formal concept analysis as mathematical theory of concept and concept hierarchies, Lecture Notes in Artificial Intelligence, 3626 (2005), pp.1-33.

      [22] O. Y. Al-Jarrah, P. D. Yoo, S. Muhaidat, G. K. Karagiannidis and K. Taha, Efficient machine learning for big data: A review, Big Data Research, 2(3) (2015), pp.87-93.

      [23] Changwon. Y, Luis. Ramirez and Juan. Liuzzi, Big data analysis using modern statistical and machine learning methods in medicine, International Neurourology Journal, 18 (2014), pp.50-57.

      [24] P. Singh and B. Suri, Quality assessment of data using statistical and machine learning methods. L. C.Jain, H. S.Behera, J. K.Mandal and D. P.Mohapatra (eds.), Computational Intelligence in Data Mining, 2 (2014), pp. 89-97.

      [25] A. Jacobs, The pathologies of big data, Communications of the ACM, 52(8) (2009), pp.36-44.

      [26] H. Zhu, Z. Xu and Y. Huang, Research on the security technology of big data information, International Conference on Information Technology and Management Innovation, 2015, pp.1041-1044.

      [27] Z. Hongjun, H. Wenning, H. Dengchao and M. Yuxing, Survey of research on information security in big data, Congresso da sociedada Brasileira de Computacao, 2014, pp.1-6.

      [28] I. Merelli, H. Perez-sanchez, S. Gesing and D. D.Agostino, Managing, analysing, and integrating big data in medical bioinformatics: open problems and future perspectives, BioMed Research International, 2014, (2014), pp.1-13.

      [29] Demirkan, H., Delen, D.: Leveraging the capabilities of service-oriented decision support systems: putting analytics and big data in cloud. Decis. Support Syst. 55, 412–421 (2013)

      [30] Chen, M., Mao, S., Liu, Y.: Big data: a survey. Mobile Netw. Appl. 19, 171–209 (2014)

      [31] Han, J., Haihong, E., Le, G., Du, J.: Survey on NoSQL database. In: Proceedings of the 2011 6th International Conference on Pervasive Computing and Applications (ICPCA), Port Elizabeth, pp. 363–366 (2011)

      [32] C. L. Philip, Q. Chen and C. Y. Zhang, Data-intensive applications, challenges, techniques and technologies: A survey on big data, Information Sciences, 275 (2014), pp.314-347.

      [33] M. Herland, T. M. Khoshgoftaar and R. Wald, A review of data mining using big data in health informatics, Journal of Big Data, 1(2) (2014), pp. 1-35.

      [34] [34] T. Huang, L. Lan, X. Fang, P. An, J. Min and F. WangPromises and challenges of big data computing in health sciences, Big Data Research, 2(1) (2015), pp. 2-11.

      [35] G. Ingersoll, Introducing apache mahout: Scalable, commercial friendly machine learning for building intelligent applications, White Paper, IBM Developer Works, (2009), pp. 1-18.

      [36] H. Li, G. Fox and J. Qiu, Performance model for parallel matrix multiplication with dryad: Dataflow graph runtime, Second International Conference on Cloud and Green Computing, 2012, pp.675-683.

      [37] D. P. Acharjya, S. Dehuri and S. Sanyal Computational Intelligence for Big Data Analysis, Springer International Publishing AG, Switzerland, USA, ISBN 978-3-319-16597-4, 2015.9

      [38] G. Ramprabu, S. Nagarajan, “Design and Analysis of Novel Modified Cross Layer Controller for WMSNâ€, Indian Journal of Science and Technology, Vol 8(5), March 2015, pp.438-444.

  • Downloads

  • How to Cite

    S, C., & R, P. (2018). A Review on Latest Technologies in Big Data Analysis. International Journal of Engineering & Technology, 7(3.1), 93-97. https://doi.org/10.14419/ijet.v7i3.1.16806

    Received date: 2018-08-04

    Accepted date: 2018-08-04

    Published date: 2018-08-04