Business Intelligence and Decision Support Using Distinct MapReduce with Access Patterns (DMRAP) in Big Data Analytics

In modern business environment, data is playing a vital role in taking quick decisions. The growth of
data is uncertain and huge over period of time and also handling such huge amount of unstructured and structured
data is difficult for business analytics, for quick decision making we need efficient tools that are capable of handling
and processing Bigdata for analytical purposes. Traditional available systems such as data mining techniques,
association rules and other mining methodologies are insufficient to store, handle and process the spontaneous
amount of data generated day by day. Even though Hadoop, MapReduce and MapReduce Access Patterns (MRAP)
involved in increasing the productivity and fault tolerance, improving the performance is complicated task for
handling the vast amount of data. So, we propose a new methodology Distinct MapReduce with Access Patterns (DMRAP) in association with Hadoop MapReduce and business intelligence tools that supports in taking quick
decisions and are capable of handling and processing Big data. The methodology used here is unique and more
tangible, scalable, reduces cost and time for wide variety of data analytics, real time operations in order to predict
and forecast the future business needs.