A SYSTEM TO ANALYSIS REAL TIME BIGDATA USING TOPDOWN SPECIALIZATION
Keywords:Cloud, data anonymization, data partition, privacy preservation, Top Down Specifications (TDS)
The large number of cloud services such as like Application,Platform,Infrastructure (IaaS,PaaS,SaaS)requires the users want to share the private data like for data analysis or data mining, data anonymization, bringing privacy concerns privacy may be data sets via generalization to satisfy certain security and privacy requirements such as k-anonymity and store them categorized of preserving techniques. At present nowadays the data cloud applications are increasing their large-scale data within the Big data trend huge amount of data sets and preserving sensitive, large scale data is very difficult data sets due to their map reduce is design by two phase of this technique to archieve scalable two phase Top Down Specifications (TDS) is scalability and efficient (TDS) is significance improved over existing approaches. TheMap reduce approach is a framework and this widely adopted for parallel data processing to address the scalability problem of the top-down specialization(TDS) approach for large scale data Anonymization. TDS approach is widely used for data Anonymization that provides a good arbitrate between data utility and data consistency. Most of the TDS algorithm is centralized, that are insufficient to handle large-scale data sets. we introduce a highly scalable two phase TDS approach for data Anonymization by using the map reduce framework on cloud.
I.  D. Agrawal, S. Das, and A. E. Abbadi, “Big Data and cloud computing: Current state and future opportunities,” in Proc. Int. Conf. Extending Database Technol. (EDBT), 2011, pp. 530– 533.
II.  J. Cohen, B. Dolan, M. Dunlap, J. M. Heller stein, and C. Welton, “Madskills: New analysis practicefor Big Data,” PVLDB, vol. 2, no. 2,pp. 1481–1492, 2009.
III.  J. Dean and S. Ghemawat, “Map reduce: Simplified data processing on large clusters,” Common. ACM, vol. 51, no. 1, pp. 107–113, 2008.
IV.  H. Herodotou et al., “Starfish: A self-tuning system for Big Data analytics,” inProc. 5th Int. Conf. Innovative Data Syst. Res. (CIDR), 2011, pp. 261–272.
V.  K. Michael and K. W. Miller, “Big Data: New opportunities and new challenges [guest editors’ introduction],” IEEE Comput., vol. 46, no.
VI.  pp. 22– 24, Jun. 2013.  C. Eaton, D. Deroos, T. Deutsch, G. Lapis, and P. C. Zikopoulos, Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. New York, NY, USA: Mc Graw-Hill, 2012.
VII.  R. D. Schneider, Hadoop for Dummies Special Edition. Hoboken, NJ, USA: Wiley, 2012.
VIII.  A. Cuzzocrea, D. Saccà, and J. D. Ullman, “Big Data: A research agenda,” in Proc. Int. Database Eng. Appl. Symp. (IDEAS’13), Barcelona, Spain, Oct. 09–11, 2013.
IX.  R. A. Schowengerdt, Remote Sensing: Models and Methods for Image Processing, 2nd ed. New York, NY, USA: Academic Press, 1997.
X.  D. A. Landgrebe, Signal Theory Methods in Multispectral Remote Sensing. Hoboken, NJ, USA: Wiley, 2003.
XI.  C.-I. Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification. Norwell, MA, USA: Kluwer, 2003.
XII.  J. A. Richards and X. Jia, Remote Sensing Digital Image Analysis: An Introduction. New York, NY, USA: Springer, 2006.
XIII.  J. Shi, J. Wu, A. Paul, L. Jiao, and M. Gong, “Change detection in synthetic aperture radar image based on fuzzy active contour models and genetic algorithms,” Math. Prob. Eng., vol. 2014, 15 pp., Apr.2014.
XIV.  A. Paul, J. Wu, J.-F. Yang, and J. Jeong, “Gradient-based edge detection for motion estimation in H.264/AVC,” IET Image Process., vol. 5, no. 4, pp. 323–327, Jun. 2011.
XV.  A. Paul, K. Bharanitharan, and J.-F.Wang, “Region similarity based edge detection for motion estimation in H.264/AVC,” IEICE Electron. Express, vol. 7, no. 2, pp. 47–52, Jan. 2010.
How to Cite
Copyright (c) 2021 International Education and Research Journal (IERJ)
This work is licensed under a Creative Commons Attribution 4.0 International License.