An Efficient Procedure for Classification of Big Data Using The Deep Learning Enabled Spark Architecture and Machine Learning
Anil Kumar Vishwanath Brahmane1, B. Chaitanya Krishna2

1Mr. Anil Kumar Vishwanath Brahmane, Research Scholar, Department of Computer Science and Engineering, K L Deemed to be University, (A.P), India.
2Dr. B. Chaitanya Krishna, Professor, Department of Computer Science and Engineering, K L Deemed to be University, (A.P), India.
Manuscript received on 16 October 2019 | Revised Manuscript received on 25 October 2019 | Manuscript Published on 02 November 2019 | PP: 2662-2668 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B13250982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1325.0982S1119
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Abstract: At present the Big Data applications, for example, informal communication, therapeutic human services, horticulture, banking, financial exchange, instruction, Facebook and so forth are producing the information with extremely rapid. Volume and Velocity of the Big information assumes a significant job in the presentation of Big information applications. Execution of the Big information application can be influenced by different parameters. Expediently search, proficiency and precision are the a portion of the overwhelming parameters which influence the general execution of any Big information applications. Due the immediate and aberrant inclusion of the qualities of 7Vs of Big information, each Big Data administrations anticipate the elite. Elite is the greatest test in the present evolving situation. In this paper we propose the Big Data characterization way to deal with speedup the Big Data applications. This paper is the review paper, we allude different Big information advancements and the related work in the field of Big Data Classification. In the wake of learning and understanding the writing we discover the holes in existing work and techniques. Finally we propose the novel methodology of Big Data characterization. Our methodology relies on the Deep Learning and Apache Spark engineering. In the proposed work two stages are appeared; first stage is include choice and second stage is Big Data Classification. Apache Spark is the most reasonable and predominant innovation to execute this proposed work. Apache Spark is having two hubs; introductory hubs and last hubs. The element choice will be occur in introductory hubs and Big Data Classification will happen in definite hubs of Apache Spark.
Keywords: Big Data Classification, Deep Learning, Apache Spark.
Scope of the Article: Classification