Knowledge acquisition using parallel rough set and mapreduce from big data

These days, the volume of information is developing at an uncommon rate, enormous information mining, and learning revelation have turned into another test in the time of information mining and machine learning. Large set hypothesis for learning procurement has been effectively connected in information mining. The Map Reduce strategy got more consideration from academic group and also industry for its pertinence in unstructured huge information examination. Clusters are viably utilized for parallel handling application and obtained information speak to into different groups. In this venture we have introduced working and execution stream of the Map Reduce programming ideal model with map and reduce capacity and unpleasant set hypothesis. In this work we have design distributed system. Likewise quickly talk about diverse issues and difficulties that are confronted by Map Reduce while taking care of the huge data. Also, finally we have introduced a few focal points of the Map reduce Programming model.