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2016 | 46 | 65-76
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A survey on various applications and challenges of big data analytics and its security methods

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Big-data is loosely distributed processing applications that operate large amount of knowledge. The growing power of Big Data assume the significance of analyzing huge amount of data with a frequent and quick rate of growth and change in databases and data warehouse. MapReduce is most common framework for large-scale information analytics chiefly owing to its salient options like quantifiability, fault-tolerance, easy programming, and adaptability. Apache Hadoop is an emerging technology that is widely used in the data intensive applications like Big Data Analysis. This technology is currently used in the searching applications of Google, Yahoo, and Amazon. Big data applications are an incredible advantage to associations, business, organizations and numerous huge scale and little scale businesses. Cloud computing assumes an extremely essential part in protecting information, applications and the related foundation with the assistance of strategies, technologies, controls, and enormous information tools. In addition, cloud computing, big data and its applications, advantages are liable to speak to the most promising new frontiers in science. This paper presents a summary of the distinctive options that differentiate massive information from traditional datasets. Additionally, the application of massive data analytics within the E-commerce and also the varied technologies that build analytics of client information potential is mentioned. It also summarizes about the need for Big data in Health care and in Government. Issues or challenges to big data analytics are discussed in greater level of concern. Protection and Security methods such as Vormetric Encryption, Data Security Platform, Encryption and Key Management, Fine-grained Access Controls, Security Intelligence and Automation are explained which is used to exploit the challenges to Big data.
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  • PG Scholar, Department of CSE, Anna University Regional Campus, Coimbatore, Tamil Nadu, India,
  • PG Scholar, Department of CSE, Anna University Regional Campus, Coimbatore, Tamil Nadu, India,
  • [1] “Survey on Various Applications of Hadoop in Bioinformatics” Minerva Laishram, Computer Science & Engineering, Visveswaraya Technological University International Journal of Computer Applications 129(6), (2015).
  • [2] “CloudBurst: highly sensitive read mapping with MapReduce”, Michael C. Schatz∗ Center for Bioinformatics and Computational Biology, University of Maryland, College Park MD 20742, USA.
  • [3] “DRAW: A New Data-gRouping-AWare Data Placement Scheme for Data Intensive Applications With Interest Locality” Jun Wang, Qiangju Xiao, Jiangling Yin, and Pengju Shang. Department of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32826 USA, IEEE TRANSACTIONS ON MAGNETICS, 49(6) June 2013.
  • [4] “Big Data Computing and Clouds: Challenges, Solutions, and Future Directions” Assuncoa, M.D. et al., 2013. arXiv, 1(1), pp. 1-39.
  • [5] “Big Data-State of the Art”Chalmers, S., Bothorel, C. & CLEMENTE, R., 2013. Thesis. Brest: Telecom Bretagne, Institut Mines-Telecom.
  • [6] “Using Big Data Analytics in Information Technology (IT) Service Delivery Internet Technologies and Applications Research”, Fung, H.P, 2013. 1(1), pp. 6-10.
  • [7] “MapReduce: Simplied Data Processing on Large Clusters. Communications of the ACM”, Dean, J. & Ghemawat, S., 51(1) (2008) 107-13.
  • [8] “Twister: A runtime for iterative MapReduce”, J. Ekanayake et al, In Proceedings of the 19th ACM HPDC, pages 810-818, 2010.
  • [9] “On the energy (in) efficiency of hadoopclusters”, J. Leverich et al, ACM SIGOPS Operating Systems Review, 44(1): 61-65, 2010.
  • [10] “Cloudblast: Combining mapreduce and virtualization on distributed resources for bioinformatic applications”, A. Matsunaga et al. In Fourth IEEE International Conference on eScience, pages 222-229, 2008.
  • [11] “Processing Theta-Joins using MapReduce”, A. Okcan et al. In Proceedings of the 2011 ACM SIGMOD, 2011.
  • [12] A. Pavlo et al, In Proceedings of the ACM SIGMOD, pages 165-178, 2009.
  • [13] “A Survey of Big Data Cloud Computing Security”, Elmustafa Sayed Ali Ahmed and Rashid A.Saeed, International Journal of Computer Science and Software Engineering 3(1) (2014).
  • [14] Intel IT centre, “Peer Research Big Data Analytics “, Intel’s IT Manager Survey on How Organizations Are Using Big Data, AUGUST 2012.
  • [15] “SECURITY ISSUES ASSOCIATED WITH BIG DATA IN CLOUD COMPUTING”, Venkata Narasimha Inukollu, Sailaja Arsi ,and Srinivasa Rao Ravuri, International Journal of Network Security & Its Applications 6(3) (2014).
  • [16] “Security issues associated with big data in cloud computing”, R. Saranya, V.P. Muthu Kumar, International Journal of Multidisciplinary Research and Development, (4) (2015) 580-585.
  • [17] “Securing Big Data: Security Recommendations for Hadoop and NoSQL Environments." Securosis blog, version1.0 (2012).
  • [18] Khalid A (2010). Cloud Computing: applying issues in Small Business. International Conference on Signal Acquisition and Processing (ICSAP’10), 278-281.
  • [19] Kilzer, Ann, Emmett Witchel, Indrajit Roy, Vitaly Shmatikov and Srinath T.V Setty, "Airavat: Security and Privacy for MapReduce".
  • [20] Yanglin Ren, Monitoring patients via a secure and mobile healthcare system, IEEE Symposium on wireless communication, 2011.
  • [21] Dai Yuefa, Wu Bo, Gu Yaqiang, Data Security Model for Cloud Computing, International Workshop on Information Security and Application, 2009.
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