CHALLENGES AND APPROACHES IN MACHINE LEARNING WITH BIG DATA
DOI:
https://doi.org/10.59367/9f44fw60Keywords:
Machine Learning, Big Data, Literacy, Challenges ApproachesAbstract
The Big Data revolution promises to transfigure how we live, work, and suppose by enabling process optimization, empowering sapience discovery and perfecting decision timber. The consummation of this grand eventuality relies on the capability to prize value from similar massive data through data analytics; machine literacy is at its core because of its capability to learn from data and give data driven perceptivity, opinions, and prognostications. still, traditional machine learning approaches were developed in a different period, and therefore are grounded upon multiple hypotheticals, similar as the data set befitting entirely into memory, what unfortunately no longer holds true in this new environment. These broken hypotheticals, together with the Big Data characteristics, are creating obstacles for the traditional ways. Accordingly, this paper compiles, summarizes, and organizes machine literacy challenges with Big Data. In discrepancy to other exploration that discusses challenges, this work highlights the cause – effect relationship by organizing challenges according to Big Data Vs or confines that instigated the issue volume, haste, variety, or veracity. also, arising machine learning approaches and ways are bandied in terms of how they're able of handling the colorful challenges with the ultimate ideal of helping interpreters elect applicable results for their use cases. Eventually, a matrix relating the challenges and approaches is presented. Through this process, this paper provides a perspective on the sphere, identifies exploration gaps and openings, and provides a strong foundation and stimulant for farther exploration in the field of machine literacy with Big Data.
References
R. Krikorian. (2010). Twitter by the Numbers, Twitter. [Online].Available: http://www.slideshare.net/raffikrikorian/twitter-by-the-numbers? ref=http://techcrunch.com/2010/09/17/twitter-seeing-6-billion-api-callsper-day-70k-per-second/
ABI. (2013). Billion Devices Will Wirelessly Connect to the Internet of Everything in 2020, ABI Research. [Online]. Available: https://www.abiresearch.com/press/more-than-30-billion-devices-willwirelessly-conne/
W. Raghupathi and V. Raghupathi, ‘‘Big data analytics in healthcare: Promise and potential,’’ Health Inf. Sci. Syst., vol. 2, no. 1, pp. 1–10, 2014.
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 Res., vol. 2, no. 3, pp. 87–93, Sep. 2015.
M. A. Beyer and D. Laney, ‘‘The importance of ‘big data’: A definition,’’ Gartner Research, Stamford, CT, USA, Tech. Rep. G00235055, 2012.
V. Mayer-Schönberger and K. Cukier, Big Data: A Revolution That Will Transform How We Live, Work, and Think. Boston, MA: Houghton Mifflin Harcourt, 2013.
H. V. Jagadish et al., ‘‘Big data and its technical challenges,’’ Commun. ACM, vol. 57, no. 7, pp. 86–94, 2014.
M. James, C. Michael, B. Brad, and B. Jacques, Big Data: The Next Frontier for Innovation, Competition, and Productivity. New York, NY: McKinsey Global Institute, 2011.
M. Rouse. (2011). Machine Learning Definition. [Online].Available: http://whatis.techtarget.com/definition/machine-learning
M. Rouse. (2009). Predictive Analytics Definition. [Online].Available: http://searchcrm.techtarget.com/definition/predictive-analytics
K. Grolinger, M. Hayes, W. A. Higashino, A. L’Heureux, D. S. Allison, and M. A. M. Capretz, ‘‘Challenges for MapReduce in big data,’’ in Proc. IEEE World Congr. Services (SERVICES), Jun. 2014, pp. 182–189.
J. Dean and S. Ghemawat, ‘‘MapReduce: Simplified data processing on large clusters,’’ in Proc. 6th Symp. Oper. Syst. Design Implement., 2004, pp. 137–149.
K. Shvachko, H. Kuang, S. Radia, and R. Chansler, ‘‘The hadoop distributed file system,’’ in Proc. IEEE 26th Symp. Mass Storage Syst. Technol. (MSST), May 2010, pp. 1–10.
M. M. Najafabadi, F. Villanustre, T. M. Khoshgoftaar, N. Seliya, R. Wald, and E. Muharemagic, ‘‘Deep Learning Applications and Challenges in Big Data Analytics,’’ J. Big Data, vol. 2, no. 1, p. 1, Feb. 2015.
C. Parker, ‘‘Unexpected challenges in large scale machine learning,’’ in Proc. 1st Int. Workshop Big Data, Streams Heterogeneous Source Mining Algorithms, Syst., Programm. Models Appl. (BigMine), 2012, pp. 1–6.
S. R. Sukumar, ‘‘Machine learning in the big data era: Are we there yet?’’ in Proc. 20th ACM SIGKDD Conf. Knowl. Discovery Data Mining, Workshop Data Sci. Social Good (KDD), 2014, pp. 1–5.
J. Qiu, Q. Wu, G. Ding, Y. Xu, and S. Feng, ‘‘A survey of machine learning for big data processing,’’ EURASIP J. Adv. Signal Process., vol. 67, pp. 1–16, Dec. 2016.
X.-W. Chen and X. Lin, ‘‘Big data deep learning: Challenges and perspectives,’’ IEEE Access, vol. 2, pp. 514–525, 2014.
A. Gandomi and M. Haider, ‘‘Beyond the hype: Big data concepts, methods, and analytics,’’ Int. J. Inf. Manage., vol. 35, no. 2, pp. 137–144, Apr. 2015.
D. Singh and C. K. Reddy, ‘‘A survey on platforms for big data analytics,’’ J. Big Data, vol. 2, no. 1, pp. 1–20, 2015.
P. D. C. de Almeida and J. Bernardino, ‘‘Big data open source platforms,’’ in Proc. IEEE Int. Congr. Big Data, Jun. 2015, pp. 268–275.
W. Fan and A. Bifet, ‘‘Mining big data: Current status, and forecast to the future,’’ SIGKDD Explorations Newslett., vol. 14, no. 2, pp. 1–5, Dec. 2012.
X. Wu, X. Zhu, G.-Q. Wu, and W. Ding, ‘‘Data mining with big data,’’ IEEE Trans. Knowl. Data Eng., vol. 26, no. 1, pp. 97–107, Jan. 2014.
R. Narasimhan and T. Bhuvaneshwari, ‘‘Big data—A brief study,’’ Int. J. Sci. Eng. Res., vol. 5, no. 9, pp. 350–353, 2014.
Lowlesh Nandkishor Yadav, “Predictive Acknowledgement using TRE System to reduce cost and Bandwidth”IJRECE VOL. 7 ISSUE 1 (JANUARY- MARCH 2019) pg no 275-278.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Futuristic Innovation in Arts, Humanities and Management (IJFIAHM)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
