Twelfth International Conference on e-Learning & Innovative Pedagogies

Abstract

Graduate studies have outcomes that bridge the gap between the directed learning at the undergraduate level and the world of professional research. At the graduate-level of education two outcomes are desirable; the students acquire depth of knowledge in the academic topics covered and connection between the academic topic and real world applications of those academic topics. One such topic is that of the analysis of algorithms to address efficiency of their application in specific problem domains. In a recent graduate course on the analysis of algorithms, teams of students were assigned a project to research an industrial problem application. The project required an analysis of the algorithms and tools used in the problem domain. This report documents one team’s effort with this project. The purpose of this report is to identify the benefit of assigning project of this nature and predict evolving strategies in graduate pedagogy. The open source Hadoop is the implementation of the paradigm Map-Reduce for large-scale data processing. In this paper, the algorithm’s word count, the number of words in a document or passage of text, were chosen since Map-Reduce is considered the most common platform to run this type of algorithms. The experiments showed interesting results and encourage more comparisons and improvement to enhance the algorithm performance.