Evolution Mining: A Novel Accelerated Framework for Large Data processing in the big Data Paradigm
The big data focus on the mining of the large volume, complex and exploiting datain the large scale application and Social network communities. The map reduce paradigm is been used to handle large scale data in the big data paradigm through classification and clustering algorithm as it is treated as computational intensive task. The Data Classification and Clustering algorithm using supervised and unsupervised learning models failed in processing dynamic updates as concept and feature evolution to map reduce paradigm in the large streaming application. Therefore it becomes mandatory to build a new accelerated framework which is capable of handling of the data evolution in the large data. In this Paper, we propose a novel technique named as "EvolutionMining" which develop a deep learning model in the map reduce paradigm using key value pair processing &Semantic Mining techniques. Sophisticated iterative models for feature extraction, classification and feature or concept evolution determination been devised. The proposed Technique is capable of handling the diverse data by producing the increased value to the computation characteristics such as accuracy and complexity. The Experimental results prove that proposed System outperforms other state of approaches using evaluation metrics such Runtime &Mean Error and reduces I/O overhead to much extent. Index Terms Big Data, Map reduce Paradigm, Key Value Pair, Incremental Processing