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Challenges for De-Identification Policies using MapReduce in Big Data
Abstract
Many data owners are obligatory to release the data in a variability of real world application, since it is of prominence to discovery valuable evidence stay behind the data. The motivation for this is the high scalability of the MapReduce prototype which allows for enormously parallel and distributed accomplishment over a huge number of computing nodes. This paper identifies MapReduce disputes and trials in handling Big Data with the separated of providing an suggestion of the field, simplifying better planning and management of big data developments and identifying occasions for future research in this field. Big data analytics (machine learning and interactive analytics) consists of online processing, security and privacy. Besides, current efforts aimed at refining and extending MapReduce to address recognized challenges are presented. Consequently, by detecting issues and challenges MapReduce faces when handling Big data, this study inspires upcoming Big data research.
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References
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