Symmetric Matrix-based Predictive Classifier for Big Data computation and information sharing in Cloud. (November 2016) - Vennila, V.; Rajiv Kannan, A.
Highlights: To increase the rate of Big Data computation during the data extraction and search accuracy. The real value diagonal search data is evaluated for improving the prediction rate. An efficient computation and information sharing is obtained in Cloud environment using MapReduce function. Abstract: Big Data requires real-time data-intensive processing that runs on high-performance clusters. In Big Data applications, data collection has grown exponentially. It is highly complex to extract, identify and transmit information using existing software tools. Big Data applications increase the gaps in performance between legitimate classifiers. In this paper, a Parallel Symmetric Matrix-based Predictive Bayes Classifier (PSM-PBC) model is developed for efficient Big Data computation and information sharing in Cloud environment. Initially, the Tridiagonal Symmetric Matrix is constructed on distributed Big Data in parallel. This approach enables an increase in the rate of data computation using a Householder transformation. A Cross-Validated Bayes Classifier then evaluates real-value diagonal search data to improve the prediction rate. Finally, the MapReduce function on Bayes Classes provides efficient predictive analytics regarding Big Data. The experimental evaluations are conducted with Amazon EC2 Cloud Big Data sets and exhibit improvement of the prediction rate by 10.55% along with a reduction in computation time by 40.93% compared to state-of-the-art methods. Graphical abstract: