Abstract—Digital data that come from different applications such as, wireless sensor, bioinformatics next generation sequencing, and high throughput instruments are growing in high rate. Dealing with demands of analysis of ever-growing data requires new techniques in software, hardware, and algorithms. MapReduce is a programming model initiated by Google’s Team for processing huge datasets in distributed systems; it helps programmers to write programs that process big data. The aim of this paper is to investigate MapReduce research trends, and current research efforts for enhancing MapReduce performance and capabilities. This Study concluded that the research directions of MapReduce concerned with either enhancing MapReduce programming model or adopting MapReduce for deploying existing algorithm to run with MapReduce programming model.

Index Terms—Big data, distributed computing, programming model, scalability.

A. Elsayed is with Central Laboratory for Agriculture Expert Systems, Agriculture Research Center, Cairo, Egypt (e-mail: master_abdo@yahoo.com)
O. Ismail and Mohamed E. El-Sharkawi are with the Faculty of Computers and Information, Cairo University, Cairo, Egypt (e-mail: Osama@fci-cu.edu.eg, m.elsharkawi@fci-cu.edu.eg).

 

Cite:Abdelrahman Elsayed, Osama Ismail, and Mohamed E. El-Sharkawi, "MapReduce: State-of-the-Art and Research Directions," International Journal of Computer and Electrical Engineering vol. 6, no.1, pp. 34-39, 2014.