Collaborative Group Decision Making Process using Air traffic-flow management

International Journal of Computer Science and Engineering
© 2017 by SSRG - IJCSE Journal
Volume 4 Issue 1
Year of Publication : 2017
Authors : K.C.Gayathri, C.Premkumar

How to Cite?

K.C.Gayathri, C.Premkumar, "Collaborative Group Decision Making Process using Air traffic-flow management," SSRG International Journal of Computer Science and Engineering , vol. 4,  no. 1, pp. 8-13 , 2017. Crossref,


Initially linear dataset is formed for efficient retrieval of data from a huge database. Before undergoing the process of knowledge discovery feature reduction process is implemented. This reduces the dimensionality and increases the space of data storage. Hence the map reduce is processed for the next step in knowledge discovering process to remove unwanted and irrelevant data from the database. The Support Vector Machine is one of the classifications technique is used. This overcome the problem of k Means disadvantage, it does not support effectively for both linear and nonlinear format of data. Map Reduce method to add privacy to a huge database can be obtained by adding dual authentication technique which ensures the privacy of the user without over heading the process. This overcomes the overlapping issue caused by the k means algorithm and it also reduces the issue of finding the distance between the record and cluster.


Mapreduce, Group Decision, Support Vector Machine, Air Traffic-Flow Management.


[1] J.Manyika et al. “Big data: The next frontier for innovation, competition, and productivity,”McKinsey& Company Publications , 2011.
[2] C. Lynch, “Big Data: How Do Your Data Grow?,” CNI Publication, vol. 455, no. 7209, pp. 28-29, 2008.
[3] Watkins, Andrew B, “Exploiting immunological metaphors in the development of serial, parallel, and distributed learning algorithms”, Diss. University of Kent at Canterbury, 2005.
[4] Liu, Bing ,“Opinion mining and sentiment analysis,” Proc. Springer Berlin Heidelberg, vol.2, pp. 459-526, Jan. 2011.
[5] Zhao, Zhi-Dan, and Ming-Sheng Shang, "User-based collaborative-filtering recommendation algorithms on hadoop," Proc. IEEE 3rd International Conference on Knowledge Discovery and Data Mining, vol. , pp. 478-481,Jan. 2010.
[6] G. Linden, B. Smith, and J. York, “ Recommendations: Item-to-Item Collaborative Filtering”, IEEE Trans.Internet Computing, vol. 7, no. 1, pp. 76-80, Jan. 2003.
[7] M. Bjelica, “Towards TV Recommender System Experiments with User Modeling,” IEEE Trans. Consumer Electronics, vol. 56, no. 3,pp. 1763-1769, Aug. 2010.
[8] M. Alduan, F. Alvarez, J. Menendez, and O. Baez, “Recommender System for Sport Videos Based on User Audiovisual Consumption,” IEEE Trans. Multimedia, vol. 14, no. 6, pp. 1546- 1557, Dec. 2012.
[9] Sikka R, Dhankhar A, Rana C.,“A survey paper on e-learning recommender system,” International Journal of Computer Applications, vol. 47,no. 9, pp. 27-30, Jun. 2012 .
[10] Lam, Chuck, “Hadoop in action,”Manning Publications Co., 2010.
[11] Ghemawat, Sanjay, Howard Gobioff, Shun-Tak Leung, “The Google file system,” In ACM SIGOPS Operating Systems Review, vol. 37, No. 5, pp. 29-43,2003.
[12] Turney and Peter D.,“semantic orientation applied to unsupervised classification of reviews,” In Proceedings of the 40th annual meeting on association for computational linguistics, pp. 417- 424,2002.
[13] Meng, S., Dou, W., Zhang, X., & Chen, J., “ KASR: A Keyword-Aware Service Recommendation Method on MapReduce for Big Data Applications” , IEEE Trans. Parallel and Distributed Systems, vol.25, no.12, pp. 3221-3231, Dec. 2014.
[14] Singam, J. Amaithi, and S. Srinivasan, “optimal keyword search for recommender system in big data application,” ARPN Journal of Engineering and Applied Sciences, vol. 10, no. 7, April 2006.
[15] Turney and Peter D , “semantic orientation applied to unsupervised classification of reviews”, “ Proc. of the 40th annual meeting on association for computational linguistics, pp. 417-424, 2002.
[16] Zhang L., Liu B., Lim S. H., & O'Brien-Strain E., “ Extracting and ranking product features in opinion documents,” Proc. of the 23rd International Conference on Computational Linguistics: Posters () Association for Computational Linguistics, pp. 1462-1470, Aug. 2010.
[17] Hu, Minqing, and Bing Liu, “Mining opinion features in customer reviews,” AAAI ,vol. 4. no. 4, 2004.