Literature survey on existing analytical schemes to optimize the mining results using incremental map reduce

Data mining applications become stale and obsolete over time. Incremental processing is a promising approach to refresh mining results. It utilizes previously saved states to avoid the expense of re-computation from scratch. The scope of this project is a novel incremental processing extension to Map Reduce, the most widely used framework for mining big data. The term big data deals with the act of gathering and storing large amounts of information for eventual analysis and Map-Reduce is a programming model for processing and generating a large amount of data in parallel time. In order to reduce response time, an algorithm termed Naïve Bayes is implemented, which provides more energy and fewer maps.