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International Journal Of Engineering, Business And Management(IJEBM)

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  3. Vol-7,Issue-4, July - August 2023
  4. Understand the Idea of Big Data and in Present Scenario

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Understand the Idea of Big Data and in Present Scenario

Chirag Kumar Dilipbhai Patel , Dr. Prasadu Peddi


International Journal of Engineering, Business And Management(IJEBM), Vol-7,Issue-4, July - August 2023, Pages 63-68 , 10.22161/ijebm.7.4.10

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Article Info: Received: 07 Jul 2023; Received in revised form: 06 Aug 2023; Accepted: 15 Aug 2023; Available online: 25 Aug 2023

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Big data analytics and deep learning are two of data science's most promising areas of convergence. The importance of Big Data has grown recently as several organizations, both public and commercial, have been amassing large amounts of region-specific data that may provide useful information on topics like as national information, advanced security, blackmail area, development, and prosperity informatics. For Big Data Analytics, where data is often unstructured and unlabeled, Deep Learning's ability to analyze and learn from large amounts of data on its own is a crucial feature. In this review, we look at how Deep Learning can be used to solve some of the most pressing problems in Big Data Analytics, including model isolation from large data sets, semantic querying, data marking, smart data recovery, and the automation of discriminative tasks.

Big Data, Present scenario, Google Trends, Artificial Intelligency.

[1] Stefan Strau (2018) “From Big Data to Deep Learning: A Leap Towards Strong AI or ‘Intelligentia Obscura’?” Big Data Cogn. Comput. 2018, 2, 16; doi:10.3390/bdcc2030016
[2] Vargas, Rocio & Mosavi, Amir & Ruiz, Ramon. (2017). DEEP LEARNING: A REVIEW. Advances in Intelligent Systems and Computing. 5.
[3] Genge, B., Haller, P. and Kiss, I. (2017), Big data processing to detect abnormal behavior in smart grids, in ‘Smart Grid Inspired Future Technologies: First International Conference, Smart GIFT 2016, Liverpool, UK, May 19-20, 2016, Revised Selected Papers’, Springer, pp. 214–221.
[4] Hordri, Nur & Yuhaniz, Siti & Shamsuddin, Siti Mariyam. (2016). Deep Learning and Its Applications: A Review.
[5] García, S., Ramírez-Gallego, S., Luengo, J., Benítez, J.M. and Herrera, F., 2016. Big data preprocessing: methods and prospects. Big Data Analytics Journal, p. 9.
[6] Zhong, R.Y., Newman, S.T., Huang, G.Q. and Lan, S., 2016. Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives. Computers & Industrial Engineering journal, pp. 572-591.
[7] Dittrich, J. and Quiane-Ruiz, J.-A. (2012), ‘Efficient big data processing in hadoop ´ mapreduce’, Proceedings of the VLDB Endowment 5(12), 2014–2015.
[8] Chen, Y., Alspaugh, S. and Katz, R. (2012), ‘Interactive analytical processing in big data systems: A cross-industry study of mapreduce workloads’, Proceedings of the VLDB Endowment 5(12), 1802–1813.
[9] Humphreys, G., Houston, M., Ng, R., Frank, R., Ahern, S., Kirchner, P. D. and Klosowski, J. T. (2002), ‘Chromium: a stream-processing framework for interactive rendering on clusters’, ACM transactions on graphics (TOG) 21(3), 693–702.
[10] Boja, C., Pocovnicu, A. and Batagan, L. (2012), ‘Distributed parallel architecture for” big data”’, Informatica Economica 16(2), 116.
[11] Maier, M., Serebrenik, A. and Vanderfeesten, I. (2013), ‘Towards a big data reference architecture’, University of Eindhoven.
[12] Lv, Y., Duan, Y., Kang, W., Li, Z. and Wang, F.-Y. (2015), ‘Traffic flow prediction with big data: a deep learning approach’, IEEE Transactions on Intelligent Transportation Systems 16(2), 865–873.
[13] Mestyan, M., Yasseri, T. and Kert ´ esz, J. (2013), ‘Early prediction of movie box office ´ success based on wikipedia activity big data’, PloS one 8(8), e71226
[14] Zhang, Y., Chen, M., Mao, S., Hu, L. and Leung, V. (2014), ‘Cap: Community activity prediction based on big data analysis’, Ieee Network 28(4), 52–57.
[15] Kim, Y. and Shim, K. (2012), Parallel top-k similarity join algorithms using mapreduce, in ‘Data Engineering (ICDE), 2012 IEEE 28th International Conference on’, IEEE, pp. 510–521

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