Literature survey on Big Data Analytics
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Abstract
In this latest era of computer’s, a enormous amount of data is available to managerial. Big data doesn’t only refer to datasets that are big, but also high in volume, velocity, value, veracity and variety, which is tough to handle using old-fashioned tools and methods. Due to rapid growth of such data, some ways are necessary to found to get important knowledge and values from these data sets. Also, decision makers need to gain some valuable vision from such big and endlessly changing data, ranging from daily transactions to customer connections and data of social network. Such vision can be given using Big Data Analytics, which is the application of Advanced Analytics Technique on big data. This paper aims to literature of some of the analytics methods and tools which can be applied to big data, as well as the charge provided by the applications of big data analytics in different decision domain. We have discussed the different processing techniques for big data and processing steps as well.
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References
M.H.Padgavankar, Dr.S.R.Gupta, Big Data Storage and Challenges, M.H.Padgavankar, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (2) , 2014, 2218-2223.
. Sabia, Sheetal Kalra, Applications of big Data: Current Status and Future Scope, International Journal on Advanced Computer Theory and Engineering (IJACTE), , Volume -3, Issue -5, 2014, ISSN 2319-2526.
Apache Hadoop. What Is Apache Hadoop?, 2014. http://hadoop.apache.org/, accessed April 2014.
H S. Bhosale1, Prof. D. P. Gadekar2, A Review Paper on Big Data and Hadoop, International Journal ofScientific and Research Publications, 4(10),2014.
C.Jin, R.Liu, Z.Chen, Alok Choudhary, A Scalable Hierarchical Clustering Algorithm Using Spark, IEEE,
Christos Doulkeridis, Kjetil , A Survey of LargeScale Analytical Query Processing in MapReduce, TheVLDB Journal manuscript No.5.
. Lekha R.Nair, DR. Sujala,D.Shetty, streaming Twitter Data Analysis Using Spark For Effective Job Search, Journal of Theoretical and Applied Information Technology ,. Vol.80. No. 2 2005 – 2015.
Satish Gopalani,Rohan Arora, Comparing Apache Spark and Map Reduce with Performance Analysis using K-Means, International Journal of Computer Applications Volume 113 – No. 1, March 2015. (0975 –8887)
D. Rajasekar, C. Dhanamani, S. K. Sandhya, A Survey on Big Data Concepts and Tools
Suresh Lakavath, Ramlal Naik L, A Big Data Hadoop Architecture for Online Analysis, International Journal of Computer Science and Information Technology & Security (IJCSITS), Vol. 4, No.6, December 2014, ISSN: 2249-9555.
M. Dhavapriya, N. Yasodha, Big Data Analytics: Challenges and Solutions Using Hadoop, Map Reduce and Big Table, International Journal of Computer Science Trends and Technology (IJCST) – Volume 4 Issue 1, Jan - Feb 2016
H.HU1, Y. WEN 2 , TAT-SENG CHUA1, AND XUELONG LI 3, Toward Scalable Systems for Big Data Analytics, A Technology Tutorial, IEEE, 2 ,655-687, 2014.
Ambika P R, Dr. K.N. Narasimha Murthy, Sowmya Naik PT, Aparna J S, Big Data: Towards Next Generation Analytics, International Journal of Innovative Research in Computer and Communication Engineering. Vol.3, Special Issue 5, May 2015.50 | P a g e
. Zaharia, M. Chowdhury, T. Das, A. Dave, J. Ma, M. McCauley, M. Franklin, S. Shenker, and I. Stoica. Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. Technical Report UCB/EECS-2011-82, EECS Department, University of California, Berkeley, 2011
Reynold Xin, Joshua Rosen, Matei, Zaharia, Michael J. Franklin, Scott Shenker, Ion Stoica. Shark: SQL and Rich Analytics at Scale. SIGMOD 2013. June 2013.