A MapReduce Approach for Processing Student Data Activity in a Peer-to-Peer Networked Setting
Jorge Miguel, Santi Caballé, Fatos Xhafa · 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC) · 2015
Collaborative and peer-to-peer networked based models generate a large amount of data from students' learning tasks. We have proposed the analysis of these data to tackle information security in e-Learning breaches with trustworthiness models as a functional requirement. In this context, the computational complexity of extracting and structuring students' activity data is a computationally costly process as the amount of data tends to be very large and needs computational power beyond of a single processor. For this reason, in this paper, we propose a complete MapReduce and Hadoop application for processing learning management systems log file data.