A Survey on Parallel Rough Set Based Knowledge Acquisition Using MapReduce from Big Data
نویسنده
چکیده
Nowadays, the volume of data is growing at an nprecedented rate, big data mining , and knowledge discovery have become a new challenge in the era of data mining and machine learning. Rough set theory for knowledge acquisition has been successfully applied in data mining. The MapReduce technique, received more attention from scientific community as well as industry for its applicability in big data analysis. In this paper we have presented working and execution flow of the MapReduce Programming paradigm with Map and Reduce function.This paper also describes the various Mapreduce implementation with their pros and cons such as Google’s MapReduce, ,YARN, Twister, phoenix etc. In this work we have also briefly discussed different issues and challenges that are faced by MapReduce while handling the Big data. And lastly we have presented some advantages of the Mapreduce Prograaming model.
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A comparison of parallel large-scale knowledge acquisition using rough set theory on different MapReduce runtime systems
a r t i c l e i n f o a b s t r a c t Nowadays, with the volume of data growing at an unprecedented rate, large-scale data mining and knowledge discovery have become a new challenge. Rough set theory for knowledge acquisition has been successfully applied in data mining. The recently introduced MapReduce technique has received much attention from both scientific community and industry for its a...
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