MapReduce algorithms for big data analysis

Abstract

As there is an increasing trend of applications being expected to deal with big data that usually do not fit in the main memory of a single machine, analyzing big data is a challenging problem today. For such data-intensive applications, the MapReduce framework has recently attracted considerable attention and started to be investigated as a cost effective option to implement scalable parallel algorithms for big data analysis which can handle petabytes of data for millions of users. MapReduce is a programming model that allows easy development of scalable parallel applications to process big data on large clusters of commodity machines. Google's MapReduce or its open-source equivalent Hadoop is a powerful tool for building such applications. In this tutorial, we will introduce the MapReduce framework based on Hadoop and present the state-of-the-art in MapReduce algorithms for query processing, data analysis and data mining. The intended audience of this tutorial is professionals who plan to design and develop MapReduce algorithms and researchers who should be aware of the state-of-the-art in MapReduce algorithms available today for big data analysis. © 2013 Springer-Verlag Berlin Heidelberg.