A Fast Block-level Error Identification and Rectification of Error Burst in Big data on Cloud
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Abstract
It is an undeniable fact that we need deal with huge datasets of petabytes order. So, there is a real need of processing this extent of big datasets. In current years, so many approaches have been brought to practice big data sets on cloud. But, existing systems are not very successful for rapid identification and rectification of error burst in big data. For fast and efficacious data error identification and rectification of error burst in big data sets, we bring a new and unique approach by assembling the data nodes of big data sets into clusters or blocks thereby eliminating the computation overhead involved in processing at each node. Uniquely, in our recommended proposal, operations to identify and rectify the errors can be done temporally as well as spatially on a group or cluster of nodes instead of a single node in a given big data set. Thus, the task of error identification and rectification can be accomplished faster. Arranging the data nodes into blocks is basis of our work. The recommended work can substantially lessen the time for error identification and rectification in big data sets generated by Big network systems.
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