High Performance Analytics of Bigdata Using Hadoop Framework

G.Saranya, V.Janardhan babu, G.Hemanth K .


Every organization in this era needs to make decisions on their businesses to enhance their productivity or profitability. With enterprises collecting feedback down to every possible detail, data repositories are being over flooded with information. In-order to access valuable information, these data should be processed using sophisticated statistical analysis. Traditional analytical tools, existing statistical software and data management systems find it challenging to perform deep analysis upon large data libraries and these tools rely on main memory and can function only in moderate sized data sets. Earlier distributed file systems  usually files reside on single machine ,It does not provide any reliability guarantees if that machine goes down, this means that it will only store as much information as can be stored in one machine. Users demand a service platform that can store and handle large quantities of data with some features such as easy accessibility, fast performance, durable and secure. These features can be availed without having to spend too much on hardware, upgrading, configuring etc to perform analysis of big data.

Full Text:



Yanishpradananga,shridevikarande,chandraprakashkarande: “high performance analytics of bigdata using dynamic and optimized hadoop cluster” in Proceddings of the 2016 international conference on advanced communication control and computing technologies.

Improving Decision Making in the World of Big Data http://www. forbes. com/sites/christopherfrank/2012/03/25/ improving decision-making-in-the-world-of- big-data/.

F. Bonomi, R. Milito, J. Zhu, and S. Addepalli: "Fog computing and its role in the internet of things", in Proceedings of MCC workshop on Mobile Cloud Computing (MCC '12), pp. 13-16, ACM Press, 2012

K. Shvachko, H. Kuang, S. Radia, and R. Chansler, "The Hadoop Distributed File System, " in Proceedings of the 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), 2010, pp. 1-10

Amin, A.T., Hakimi, S.L. Upper bounds on the order of a clique of a graph, SIAM Journal on Applied Mathematics. 22, 569–573 (1972)

I. M. Bomze, M. Budinich, P. M. Pardalos, and M. Pelillo. The maximum clique problem. In D. Z. Duand P. M. Pardalos, editors, Handbook of Combinatorial Optimization: Supplementary Volume A, pages 1-74. Kluwer Academic, Dordrecht, 1999.

M. Budinich. Exact bounds on the order of the maximum clique of a graph. Discrete Applied Mathematics, 127 : 535-543, 2003.

R. Diestel. Graph Theory. Springer-Verlag Heidelberg, New York, 2005.

A. Frieze, R. Kannan, and S. Vempala. Fast monte-carlo algo- rithms for finding low-rank approximations. Journal of the ACM, 51(6) : 1025-1041, 2004.

N. Halko, P. G. Martinsson, and J. A. Tropp. Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions. SIAM Review, 53(2):217-288, 2011.

J. Hastad. Clique is hard to approximate within n1−. Acta Mathematica, 182(1):105-142, 1999.

H. Ino, M. Kudo, and A. Nakamura. Partitioning of web graphs by community topology. In Proceedings of the 14th International World Wide Web Conference, pages 661-669, Chiba, Japan, 2005.

R. Kannan and S. Vempala. Spectral algorithms. Foundations and Trends in Theoretical Computer Science, 4(3–4):157-288, 2009.


  • There are currently no refbacks.

© International Journals of Advanced Research in Computer Science and Software Engineering (IJARCSSE)| All Rights Reserved | Powered by Advance Academic Publisher.