Challenges for MapReduce in Big DataProceeding of the IEEE 10th 2014 World Congress on Services (SERVICES 2014) (2014)
AbstractIn the Big Data community, MapReduce has been seen as one of the key enabling approaches for meeting continuously increasing demands on computing resources imposed by massive data sets. The reason for this is the high scalability of the MapReduce paradigm which allows for massively parallel and distributed execution over a large number of computing nodes. This paper identifies MapReduce issues and challenges in handling Big Data with the objective of providing an overview of the field, facilitating better planning and management of Big Data projects, and identifying opportunities for future research in this field. The identified challenges are grouped into four main categories corresponding to Big Data tasks types: data storage (relational databases and NoSQL stores), Big Data analytics (machine learning and interactive analytics), online processing, and security and privacy. Moreover, current efforts aimed at improving and extending MapReduce to address identified challenges are presented. Consequently, by identifying issues and challenges MapReduce faces when handling Big Data, this study encourages future Big Data research.
- Big Data,
- Big Data Analytics,
- Machine Learning,
- Interactive Analytics,
- Online Processing,
Publication DateJune 1, 2014
Citation InformationK. Grolinger, M. Hayes, W. Higashino, A. L'Heureux, D. S. Allison, M. A. M. Capretz, “Challenges for MapReduce in Big Data”, to appear in the Proc. of the IEEE 10th 2014 World Congress on Services (SERVICES 2014), June 27-July 2, 1014, Alaska, USA.