BibTeX:

@article{IJIRSTV3I2065,
     title={A System for Large-Scale Graph Processing on the Concept of Map-Reduce and MRBG},
     author={Nilesh Kurhade and Prof. Snadip Kahate},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={3},
     number={2},
     pages={81--85},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV3I2065.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

A time-line based framework for topic MapReduce programming model is widely used for large scale and one-time data intensive distributed computing, but lacks flexibility and efficiency of processing small incremental data. Incremental Mapreduce framework is proposed for incrementally processing new data of a large data set, which takes state as implicit input and combines it with new data. Map tasks are created according to new splits instead of entire splits while reduce tasks fetch their inputs including the state and the intermediate results of new map tasks from Preserved and latest generated result. The preserved states really producing the promising result and significantly reduce the run time for refreshing big data mining results compared to re-calculating on both simple and multi stage MapReduce. The intermediate states are saved in the form of kv-pair level data and data dependence in a MapReduce computation as a bipartite graph, called MRBGraph. A MRBG-Store is designed to preserve the fine-grain states in the MRBGraph and support efficient queries to retrieve fine-grain states for incremental processing.

CAN Map, Reduce, key value, MRBG