Big Data Processing with harnessing Hadoop - MapReduce for Optimizing Analytical Workloads

Now a days, we are living with social media data like heartbeat. The exponential growth with data first presented challenges to cutting-edge businesses such as Google, MSN, Flipkart, Microsoft, Facebook, Twitter, LinkedIn etc. Nevertheless, existing big data analytical models for hadoop comply with MapReduce analytical workloads that process a small segment of the whole data set, thus failing to assess the capabilities of the MapReduce model under heavy workloads that process exponentially accumulative data sizes.[1] In all social business and technical research applications, there is a need to process big data of data in efficient manner on normal uses data. In this paper, we have proposed an efficient technique to classify the big data from email using firefly and naive bayes classifier. Proposed technique is comprised into two phase, (i) Map reduce framework for training and (ii) Map reduce framework for testing. Initially, the input twitter data is given to the process to select the suitable feature for data classification. The traditional firefly algorithm is applied and the optimized feature space is adopted for the best fitting results. Once the best feature space is identified through firefly algorithm, the data classification is done using the naive bayes classifier. Here, these two processes are effectively distributed based on the concept given in Map-Reduce framework. The results of the experiment are validated using evaluation metrics namely, computation time, accuracy, specificity and sensitivity. For comparative analysis, proposed big data classification is compared with the existing works of naive bayes and neural network.