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A Survey of Big Data:Challenges and Specifications


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1 Department of Computer Engineering, University of Apadana, Shiraz, Iran, Islamic Republic of
     

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Big data is a phrase have been hearing about for a long time, but there are still ambiguities about the true nature of these enlargements. In fact, Big data are the main driving force behind the emergence of new technologies such as artificial intelligence, data, science and the Internet of Things in the age of digital metamorphic. Also, due to the increasing and expanding volume of data transmission in cyberspace and the Internet, especially in the field of banking and e-commerce, the macro data entry problem. The storage of a large amount of data items and management, preprocessing and post processing, and the speed and accuracy of information security are important and attract many researchers and IT professionals. Considering these definitions, decided to have a comprehensive definition of the nature of Big data. So in this article first define the big data, and then look at the challenges and the Specifications and uses of this topic, in the end will draw conclusions in this area.


Keywords

Big Data, Challenges, Architecture, Specifications, Challenges, Uses.
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  • Kazemi, Uranus. "Clustering methods in Big data." Journal of Embedded Systems and Processing 2.1, 2, 3 ,2017.

  • Joseph Mc Kendrick. “Big Data, Big Challenges, Big Opportunities: 2012 IOUG Big Data Strategies Survey”, IOUG, 2012.

  • Wallis, Nigel. "Big data in Canada: Challenging complacency for competitive advantage." IDC, Dec 2012.

  • Wang, L., Lu, K., Liu, P., Ranjan, R., Chen, L. “Ike-svd: dictionary learning for spatial big data via incremental atom update”. Comput. SCI. Eng. 16, 41–52 , 2014.

  • Kołodziej, J., González-Vélez, H., Wang, L. Advances in data intensive modelling and simulation. Future Gener. Comput. Syst. 37, 282–283, 2014.

  • Xue, W., Yang, C., Fu, H., Wang, X., Xu, Y., Gan, L., Lu, Y., Zhu, X., “Enabling and scaling a global shallow-water tmosphericmodel on Tianhe-2”, In: International parallel and distributed processing symposium, pp. 745–754, 2014.

  • Ma, Y.,Wang, L., Liu, D., Yuan, T., Liu, P., Zhang,W. “Distributed data structure templates for data-intensive remote sensing applications”. Concur. Comput. 25, 1784–1793, 2013.

  • Ma, Y., Wang, L., Zomaya, A.Y., Chen, D., Ranjan, R.,” Task-tree based large-scale mosaicking for massive remote sensed imageries with dynamic drag scheduling”, IEEE Trans. Parallel Distrib. Syst. 25, 2126–2135, 2014.

  • Kaisler, S., Armour, F., Espinosa, J. A. And Money, W. “Big Data: Issues and Challenges Moving Forward”, Proceedings of the 46th Hawaii International Conference on System Sciences (HICSS), pp 995-1004., 2013.

  • Matthew Smith, Christian Szongott, Benjamin Horne, Gabriele von Voigt. “Big Data Privacy Issues in Public Social Media”, IEEE, 2013.

  • Fang, R., Pouyanfar, S., Yang, Y., Chen, S. C., & Iyengar, S. S, “Computational health informatics in the big data age: A survey”, ACM Computing Surveys (CSUR), 49 (1), 12, 2016.

  • Chen, C. P., & Zhang, C. Y, “Data-intensive applications, challenges, techniques and technologies: A survey on Big Data”, Information Sciences, 275, 314-347, 2014.

  • Rabiee, F. “Focus-group interview and data analysis. Proceedings of the nutrition society”, 63 (4), 655-660, 2004.

  • Sri, P. A., & Anusha, M, “Big data-survey”, Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 4 (1), 74-80, 2016.

  • Khan, N., Yaqoob, I., Hashem, I. A. T., Inayat, Z., Ali, M., Kamaleldin, W.,... & Gani, A, “Big data: survey, technologies, opportunities, and challenges”. The Scientific World Journal, 2014.‏

  • Ward, J. S., & Barker, A, “Undefined by date: a survey of big data definitions”, arXiv preprint arXiv:1309.5821, 2013.

  • LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. “Big data, analytics and the path from insights to value”, MIT Sloan management review, 52 (2), 21, 2011.

  • Sagiroglu, S., & Sinanc, D, Big data: A review. In Collaborative Technologies and Systems (CTS), 2013 International Conference on (pp. 42-47). IEEE, 2013.

  • Singh, D., & Reddy, C. K. , “A survey on platforms for big data analytics”. Journal of Big Data, 2 (1), 8, 2015.

  • Tsai, C. W., Lai, C. F., Chao, H. C., & Vasilakos, A. V, “Big data analytics: a survey”, Journal of Big Data, 2 (1), 21, 2015.

  • Russom, P, “Big data analytics”, TDWI best practices report, fourth quarter, 19 (4), 1-34, 2011.

  • Emani, C. K., cloth, N., & Nicolle, C, “Understandable big data: a survey”, Computer science review, 17, 70-81, 2015.

  • Yu, S., Liu, M., Dou, W., Liu, X., & Zhou, S, “Networking for big data: A survey”, IEEE Communications Surveys & Tutorials, 19 (1), 531-549, 2017.

  • Hampton, S. E., Strasser, C. A., Tewksbury, J. J., Groom, W. K., Budden, A. E., Bachelor, A. L.,... & Porter, J. H, “Big data and the future of ecology”, Frontiers in Ecology and the Environment, 11 (3), 156-162, 2013.

  • Kwon, O., Lee, N., & Shin, B, “ Data quality management, data usage experience and acquisition intention of big data analytics”, International Journal of Information Management, 34 (3), 387-394, 2014.

  • Qiu, J., Wu, Q., Ding, G., Xu, Y., & Feng, S, “A survey of machine learning for big data processing”, EURASIP Journal on Advances in Signal Processing, 2016 (1), 67.

  • Wang, H., Liu, W., & Soyata, T, ”Accessing big data in the cloud using mobile devices”, In Cloud Technology: Concepts, Methodologies, Tools, and Applications (pp. 222-248). IGI Global, 2015.

  • Tsai, C. W., Lai, C. F., Chiang, M. C., & Yang, L. T, “Data mining for Internet of Things: A survey”, IEEE Communications Surveys and Tutorials, 16 (1), 77-97, 2014.

  • Akoka, J., Comyn-Wattiau, I., & Laoufi, N, “Research on Big Data–A systematic mapping study”, Computer Standards & Interfaces, 54, 105-115, 2017.

  • Lee, I, “Big data: Dimensions, evolution, impacts, and challenges”. Business Horizons, 60 (3), 293-303, 2017.

  • Ward, P. T., McCreery, J. K., Ritzman, L. P., & Sharma, D, “Competitive priorities in operations management”, Decision Sciences, 29 (4), 1035-1046, 1998.

  • Forsyth, J., & Boucher, L,. “Why big, data is not enough”, Research World, 2015 (50), 26-27, 2015.


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  • A Survey of Big Data:Challenges and Specifications

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Authors

Uranus Kazemi
Department of Computer Engineering, University of Apadana, Shiraz, Iran, Islamic Republic of

Abstract


Big data is a phrase have been hearing about for a long time, but there are still ambiguities about the true nature of these enlargements. In fact, Big data are the main driving force behind the emergence of new technologies such as artificial intelligence, data, science and the Internet of Things in the age of digital metamorphic. Also, due to the increasing and expanding volume of data transmission in cyberspace and the Internet, especially in the field of banking and e-commerce, the macro data entry problem. The storage of a large amount of data items and management, preprocessing and post processing, and the speed and accuracy of information security are important and attract many researchers and IT professionals. Considering these definitions, decided to have a comprehensive definition of the nature of Big data. So in this article first define the big data, and then look at the challenges and the Specifications and uses of this topic, in the end will draw conclusions in this area.


Keywords


Big Data, Challenges, Architecture, Specifications, Challenges, Uses.

References