Minimum Spanning Tree Algorithms in MapReduce Framework on Hadoop

Abstract

We live in a data age, it means that today there is big problem for processing the data. Data processing is required everywhere like industry, educational centers or research center. So handling of these types of huge data is big problem. Graphs are analyzed in many important contexts like page rank, protein-protein interaction networks, and analysis of social networks. Many graphs of interest are difficult to analyze because of their large size, often spanning millions of vertices and billions of edges. We believe that MapReduce has emerged as an enabling technology for large-scale distributed graph processing. Its functional abstraction provides an easy-to-understand model for designing scalable, distributed algorithms. The open-source Hadoop implementation of MapReduce has provided researchers with a powerful tool for tackling large-data problems.