HYBRID ERA ON BIG DATA ANALYTICS PLATFORMS

Authors

  • S Raju
  • E Krishna
  • G Harika
  • E Krishna

Abstract

The key purpose of this paper is to provide an unbiased assessment of different systems appropriate for vast processing of facts. Numerous technological systems available for broad knowledge analytics are analysed in this paper and comprehensive reviews are addressed on their strengths and limitations. Similarly, a broad collection of guidelines for adapting knowledge mining for massive statistical research was addressed wart its suitability to cope with actual-global computing problems. Through the successful introduction of these well developed and commonly utilized knowledge mining algorithms, the destiny patterns of big information processing and analysis can be anticipated to focus on the strengths of the technological frameworks and platforms available. Hybrid strategies (integration of or broader structures) can be best adapted for a chosen knowledge mining algorithm which can be well adaptable and can be processed in real time. Keywords huge facts; mass data analytics; cloud computing; mining statistics; computer research; systems of large facts;

References

Agneeswaran, Vijay Srinivas, PranayTonpay, and JayatiTiwary. "Paradigms for realizing machine learning algorithms." Big Data 1.4 (2013): 207—214.

Tsai, Chun-Wei, et al. "Big Data Analytics." Big Data Technologies and Applications. Springer International Publishing, 2016. 13-52.

Legend, N. and Enrage, A. "Big Data analytics: a literature review paper” ‘Industrial Conference on Data Mining', Springer, 2014, pp. 214— 227.

Wu, X., Zhu, X., Wu, G.-Q. And Ding, W. "Data mining with Big Data," IEEE transactions on knowledge and data engineering (26:1), 2014, pp. 97—107.

Zikopoulos, Paul, et al. "Harness the power of Big Data the IBM Big Data platform". McGraw Hill Professional, 2012.

Singh, D. and Reddy, C. K. "A survey on platforms for Big Data analytics," Journal of Big Data (2:1), 2014, pp. 1.

Steinmetz, Ralf, and Klaus Where. "Peer-to-peer systems and applications." LNCS Springer (2005).

Hadoop. http://hadoop.apache.org/

Lin, C.-Y., Tsai, C.-H., Lee, C.-P. And Lin, C.-J. "Large-

scale logistic regression and linear support vector machines using Spark", 'IEEE International Conference on Big Data"', IEEE, 2014, pp. 519—528.

Berkeley Data Analysis Stack. https://amplab.cs.berkeley.edu/software/

Bunya, Rajkumar. "High Performance Cluster Computing: Architecture and Systems, Volume I." Prentice Hall, Upper Saddle River, NJ, USA 1 (1999): 999.

Beckerman, Ron, Mikhail Blanco, and John Langford, eds. "Scaling up machine learning: Parallel and distributed approaches". Cambridge University Press, 2011.

Nicholls, John, and William J. Dally. "The GPU computing era." IEEE micro 30.2 (2010).

Brown, Stephen D., et al. "Field-programmable gate arrays." Vol. 180. Springer Science & Business Media, 2012.

Asuncion, M. D., Cahiers, R. N., Bianchi, S., Nett, M. A. and Bunya, R. "Big Data computing and clouds: Trends and future directions," Journal of Parallel and Distributed Computing (79), 2015, pp. 3—15.

Fan, W. and Bidet, A. "Mining Big Data: current status, and forecast to the future," ACM singed Explorations Newsletter (14:2), 2013, pp. 1—5.

Colic, V., Chafe, F., Barolo, L. and Lela, A. "Scalability, Memory Issues and Challenges in Mining Large Data Sets” ‘Intelligent Networking and Collaborative Systems (Incas), 2014 International Conference on', IEEE, 2014, pp. 268--273.

Fernandez, Alberto, et al. "Big Data with Cloud Computing: an insight on the computing environment, MapReduce, and programming frameworks." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 4.5 (2014): 380—409.

Hastie T, Tibshirani R, Friedman J. "The Elements of Statistical Learning: Data Mining, Inference and Prediction". 2nd ed. New York, NY; Berlin/Heidelberg: Springer; 2009

Han, Jiawei, et al. "Frequent pattern mining: current status and future directions." Data Mining and Knowledge Discovery 15.1 (2007): 55— 86.

Moans, S., Aksehirli, E. and Goethals, B. "Frequent itemset mining for Big Data" Big Data, 2013 IEEE International Conference on, IEEE, 2013, pp. 111—118.

Arora, S. and Chana, I. "A survey of clustering techniques for Big Data analysis" Confluence The Next Generation Information Technology Summit (Confluence), 2014 5th International Conference-, IEEE, 2014, pp. 59—65.

Kurasova, Olga, et al. "Strategies for Big Data clustering." Tools with Artificial Intelligence (ICTAI), 2014 IEEE 26th International Conference on. IEEE, 2014, pp. 740—747.

Lin, J. and Rayon, D. "Scaling Big Data mining infrastructure: the twitter experience," ACM SIGKDD Explorations Newsletter (14:2), 2013, pp. 6—19.

Guide, SAS User’s. "Statistical analysis system." SAS Institute Inc., Cary, North Carolina, USA (1986).

Python. https://www.python.org/

KNIME. https://www.knime.org/

Koliopoulos, A.-K., Yiapanis, P., Steiner, F., Nomadic, G. and Keane, J. "A parallel distributed Weka framework for Big Data mining using Spark” ‘Big Data (Big Data Congress), 2015 IEEE International Congress on', IEEE, 2015, pp. 9—

Men, Xiangrui, and et al. "Glib: Machine learning in apache spark." Journal of Machine Learning Research 17.34 (2016): 1-7.

Agawam, Aleph, et al. "A reliable effective treacle linear learning system." Journal of Machine Learning Research 15.1 (2014): 11111133.

Chu, Cheng-Tao, et al. "Map-reduce for machine learning on multicore." NIPS. Vol. 6. 2006.

Analytics, Revolution. "Packages in Readopt Toolkit." (2015).

Harrick, Mark, and Tom Plunkett. "Using R to unlock the value of Big Data: Big Data analytics with Oracle R enterprise and Oracle R connector for Hadoop". McGraw-Hill Education Group, 2013.

Zheng, Jiang, and Aldo Darning. "An initial study of predictive machine learning analytics on large volumes of historical data for power system applications." Big Data (Big Data), 2014 IEEE International Conference on. IEEE, 2014.

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Published

2020-12-30

How to Cite

Raju, S. . ., Krishna, E. . ., Harika, G. . ., & Krishna, E. . . (2020). HYBRID ERA ON BIG DATA ANALYTICS PLATFORMS. The Journal of Contemporary Issues in Business and Government, 26(3), 129–133. Retrieved from https://cibgp.com/au/index.php/1323-6903/article/view/527

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