期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2016
卷号：4
期号：3
页码：3598
DOI：10.15680/IJIRCCE.2016.0403158
出版社：S&S Publications
摘要：Programming paradigm allows for massive scalability across hundreds or thousands of servers which leads to the collection of enormous volume of data. Processing this voluminous data requires an advanced techniques like Map Reduce for efficient processing of data. MapReduce refers to two separate and distinct tasks. The first is the map job, which takes a set of data and converts it into another set of data. The reduce job takes the output from a map as input and combines those data tuples into a smaller set of tuples. As new data and updates are constantly arriving, the results of data mining applications become stale and obsolete over time. This paper proposes incremental - MapReduce a novel incremental processing extension to MapReduce, the most widely used f ramework for mining big data. It utilizes previously saved states to avoid the expense and time required for reprocessing the same set of data.We analysis Financial accounting data is used for analyzing the proposed approach and the experimental outcome sh ows the effectiveness of the proposed technique when compared to the existing system with simple MapReduce method
