A New Efficient Privacy For A Multi-Skyline Queries With Mapreduce

Badavathu Kavitha

Abstract


The skyline query technology has pulled in much consideration as of late. This is for the most part because of the significance of horizon brings about numerous applications, for example, multi-criteria basic leadership, information mining, and data suggested frameworks. The horizon administrator has pulled in impressive consideration as of late because of its wide applications. Be that as it may, registering a horizon is testing today since we need to manage enormous information. For information escalated applications, the MapReduce system has been broadly utilized as of late. In this paper, what's more, we apply the strength control sifting technique to viably prune non-horizon focuses ahead of time. We next segment information in light of the districts separated by the quad tree and figure hopeful horizon focuses for each segment utilizing MapReduce. At long last, we propose a productive technique for preparing multi-horizon questions with MapReduce with no change of the Hadoop internals. Through different analyses, we demonstrate that our approach beats past investigations by requests of extent

References


J. Lee, S. won Hwang, Z. Nie, and J.-R. Wen, “Navigation system for product search,” in ICDE, 2010.

T. Lappas and D. Gunopulos, “Efficient confident search in large review corpora,” in ECML/PKDD (2), 2010.

G. Wang, J. Xin, L. Chen, and Y. Liu, “Energy-efficient reverse skyline query processing over wireless sensor networks,” TKDE, vol. 24, no. 7, 2012.

L. Zou, L. Chen, M. T. Ozsu, and D. Zhao, “Dynamic skyline ¨ queries in large graphs,” in DASFAA, 2010.

C. Kim and K. Shim, “Supporting set-valued joins in nosql using mapreduce,” Information Systems, vol. 49, pp. 52–64, 2015.

Y. Kim and K. Shim, “Efficient top-k algorithms for approximate substring matching,” in SIGMOD, 2013, pp. 385–396.

J. Dean and S. Ghemawat, “Mapreduce: Simplified data processing on large clusters,” Communication of the ACM, vol. 51, no. 1, pp. 107–113, 2008.

K. Mullesgaard, J. L. Pedersen, H. Lu, and Y. Zhou, “Efficient skyline computation in mapreduce,” in EDBT, 2014, pp. 37–48.

B. Zhang, S. Zhou, and J. Guan, “Adapting skyline computation to the mapreduce framework: Algorithms and experiments,” in DASFAA, 2011, pp. 403–414.

J. Zhang, X. Jiang, W. S. Ku, and X. Qin, “Efficient parallel skyline evaluation using mapreduce,” IEEE Trans. Parallel Distrib. Syst., vol. 27, no. 7, pp. 1996–2009, 2016.

Y. Park, J.-K. Min, and K. Shim, “Parallel computation of skyline and reverse skyline queries using mapreduce,” VLDB, vol. 6, no. 14, pp. 2002–2013, 2013.

R. L. Graham, “Bounds on multiprocessing timing anomalies,” SIAM journal on Applied Mathematics, vol. 17, no. 2, 1969.

Y. Park, J.-K. Min, and K. Shim, “Processing of probabilistic skyline queries using mapreduce,” VLDB, vol. 8, no. 12, 2015.

“Apache hadoop,” http://hadoop.apache.org. [15] J. Chomicki, P. Godfrey, J. Gryz, and D. Liang, “Skyline with presorting,” in ICDE, 2003, pp. 717–719.

D. Kossmann, F. Ramsak, and S. Rost, “Shooting stars in the sky: An online algorithm for skyline queries,” in VLDB, 2002.

J. Lee and S.-w. Hwang, “Scalable skyline computation using a balanced pivot selection technique,” Information Systems, vol. 39, pp. 1–21, 2014.


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