A Queuing Method for Adaptive Censoring in Big Data Processing

Ayesha Banu R,, Mahamat Mahmoud Salim Breck, Kamalashree M, Pavithra Rayasam,, Aruna Kumara. B

Abstract


As per 2.5 quintillion bytes of information go on producing daily, the period of big data residues undeniably upon us.Running scrutiny on widespread datasets is an experiment. Luckily, an important proportion of the information accumulated can be misplaced although preserving a confident value of numerical implication in various cases. Censoring delivers us an expected choice used for data decrease. But, the information carefully preferred through censoring is not at all consistently, which force the computational amount of necessity. In this article, we suggested a lively, queuing techniques to evenly available the data’s dealing and surrendering the convergence act of censoring. The planned technique which involves simple and closed-form apprises, which does not take any kind of cost in rapports of accurateness relating towards the unique adaptive censoring technique. And we will be using the AES Algorithms to encrypting given Data form file systems and it will be uploading all files to data center using queuing model.

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References


K. Slavakis, G. B. Giannakis, and G. Mateos, “Modeling and optimization for big data analytics: Learning tools for our era of data deluge,” IEEE Signal Processing Magazine, vol. 31, no. 5, pp.18–31, Sep. 2014. [2] D. Donoho, “Compressed sensing,” IEEE Transactions on Information Theory, vol. 52, no. 4, pp. 1289–1306, Apr. 2006. [3] P. Drineas, M. W. Mahoney and S. Muthukrishnan, “Sampling algorithms for l2regression and applications,” in ACM-SIAM Symposium on Discrete Algorithms, pp. 1127– 1136, Jan. 2006. [4] C. Boutsidis and P. Drineas, “Random projections for the nonnegative least-squares problem,” Linear Algebra and its Applications, vol. 431, no. 5, pp. 760–771, Dec. 2008. [5] M. Mahoney, “Randomized algorithms for matrices and data,” Foundations and Trends in Machine Learning, vol. 3, no. 2, pp. 123–224, Nov. 2011. [6] C. Rago, P. Willett, and Y. Bar-Shalom, “Censoring sensors: A lowcommunicationrate scheme for distributed detection,” IEEE Trans.Aerosp. Electron. Syst., vol. 32, no. 2,pp. 554–568, Apr. 1996. [7] D. P. Woodruff, “Sketching as a tool for numerical linear algebra,” Foundations and Trends in Machine Learning, vol. 10, no. 1-2, pp. 1–157, Oct. 2014. [8] Y. Plan and R. Vershynin, “One-bit compressed sensing by linear programming,” IEEE Transactions on Signal Processing, vol. 66, no. 8, pp. 1275–1297, Aug. 2013. [9] G. Mateos, J. A. Bazerque, and G. B. Giannakis, “Distributed sparse linear regression,” IEEE Transactions on Signal Processing, vol. 58, no. 10, pp. 5262–5276, Oct. 2010. [10] E. J. Msechu and G. B. Giannakis, “Distributed measurement censoring for estimation with wireless sensor networks,” in Proc. 12th Int. Workshop Signal Process. Adv. Wireless Commun., San Francisco, CA, Jun. 2011, pp. 216–220.




DOI: https://doi.org/10.26483/ijarcs.v9i0.6246

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