A REAL TIME EFFECTIVE STORAGE SYSTEM IN HADOOP BASED MEDICAL DATA ARCHITECTURE FOR DYNAMIC FILE SELECTION VIA BIGDATA ANALYTICS

Abstract

Nowadays modern technologies have been depending on big data applications related to data storage; it is an important attribute for improving the storage of applications. The various surveys related to bigdata applications simplify the file storage system. It is clearly observed that effective file handling mechanism do not recognize according to survey. The existed methods store the unstructured and structured files with less level of sensitivity. Therefore, an advanced file handling methodology is necessary for big-data analytics. In this research work map secure reduce layers (MSR) with elastic net regression (ENR) model has implementing. The hadoop distribute file system (HDFS) is taken as file handling platform; in this MSR-ENR techniques are verified. The proposed MSR-ENR can handle the all type of memory files with various extensions (.dox, .docs, .pdf, .rar and etc.). At final calculate the performance measures such as processing time, sensitivity, accuracy, throughput and recall. This proposed MSR-ENR method outperforms the simulation results and challenging the current technologies. The performance measures such as accuracy around 95%, sensitivity 98%, specificity 99%, Recall 95%, throughput 92% and processing time 420ms are achieved, and these results are challenging the present technologies. Dataset similar to enhance the processing of patient information, Electronic Medical Records (EMR) generated by multiple medical equipment and mobile apps will be introduced into MongoDB utilizing the Hadoop framework and improvised processing approach