Abstract: A rapid growth of data in recent time, Industries and academia required an intelligent data analysis tool that would be helpful to satisfy the need to analysis a huge amount of data. MapReduce framework is basically designed to compute data intensive applications to support effective decision making. Since its introduction, remarkable research efforts have been put to make it more familiar to the users subsequently utilized to support the execution of massive data intensive applications. Our survey paper emphasizes the state of the art in improving the performance of various applications using recent MapReduce models and how it is useful to process large scale dataset. A comparative study of given models corresponds to Apache Hadoop and Phoenix will be discussed primarily based on execution time and fault tolerance. At the end, a high-level discussion will be done about the enhancement of the MapReduce computation in specific problem area such as Iterative computation, continuous query processing, hybrid database etc.

Shafali Agarwal and Zeba Khanam, “Map Reduce: A Survey Paper on Recent Expansion” International Journal of Advanced Computer Science and Applications(IJACSA), 6(8), 2015. http://dx.doi.org/10.14569/IJACSA.2015.060828

@article{Agarwal2015,
title = {Map Reduce: A Survey Paper on Recent Expansion},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2015.060828},
url = {http://dx.doi.org/10.14569/IJACSA.2015.060828},
year = {2015},
publisher = {The Science and Information Organization},
volume = {6},
number = {8},
author = {Shafali Agarwal and Zeba Khanam}
}