Machine learning approaches on map reduce for Big Data analytics

Abstract

To analyze enormous datasets, collection of algorithms, associated systems and perform necessary processing on massive data structures there is obligation for a novel trend, which is framed by Big Data. Architecture of Big Data varies across compound machines and clusters with unique purpose sub systems. The data produced from several sources requires analysis and organization with meager amounts of time. To potentially speed up the processing, a unified way of machine learning is applied on MapReduce frame work. A broadly applicable programming model MapReduce is applied on different learning algorithms belonging to machine learning family for all business decisions. By using ML algorithms with Hadoop for better storage distribution will improve the time and processing speed. This paper presents parallel implementation of various machine learning algorithms implemented on top of MapReduce model for time and processing efficiency.