An enhanced pre-processing model for big data processing: A quality framework

With the ever growing trends and technologies a huge volume of data is being evolved each and every second big data has become a supreme approach in data inception, accession, processing and analyzing the heterogeneous, huge amount of data so as to derive useful insights out of it. With data and without quality there is no point in having the data. Thus, data with quality is required to use or leverage the data in a more appropriate manner. With the evolution of big data many technologies are being developed. The input to it must be processed in such a way that the quality data yields quality effective results. An effective pre-processing model is proposed in this paper for the processing of the big data. Using relief algorithm and fast mRMR together as a hybrid approach can be used for the pre-processing of the data. Analysis shows that this hybrid approach is more effective and can greatly enhance the quality of the data. This approach can yield better performance upon the big data platform using the Spark framework.