Data management becomes a complex task when hundreds of petabytes of data are being gathered, stored and processed on a day to day basis. Efficient processing of the exponentially growing data is inevitable in this context. This paper discusses about the processing of a huge amount of data through Support Vector machine (SVM) algorithm using different techniques ranging from single node Linier implementation to parallel processing using the distributed processing frameworks like Hadoop. Map-Reduce component of Hadoop performs the parallelization process which is used to feed information to Support Vector Machines (SVMs), a machine learning algorithm applicable to classification and regression analysis. Paper also does a detailed anatomy of SVM algorithm and sets a roadmap for implementing the same in both linear and Map-Reduce fashion. The main objective is explain in detail the steps involved in developing an SVM algorithm from scratch using standard linear and Map-Reduce techniques and also conduct a performance analysis across linear implementation of SVM, SVM implementation in single node Hadoop, SVM implementation in Hadoop cluster and also against a proven tool like R, gauging them with respect to the accuracy achieved, their processing pace against varying data sizes, capability to handle huge data volume without breaking etc
M. V. Sukanya, Shiju Sathyadevan, and Sreeveni, U. B. Unmesha, “Benchmarking support vector machines implementation using multiple techniques”, in Advances in Intelligent Systems and Computing, 2015, vol. 320, pp. 227-238.