Progressive Data Analytics in Health Informatics Using Amazon Elastic Mapreduce (EMR)

Identifying, diagnosing and treatment of cancer involves a thorough investigation that involves data collection called big data from multi and different sources that are helpful for making effective and quick decision making. Similarly data analytics is used to find remedial actions for newly arriving diseases spread across multiple warehouses. Analytics can be performed on collected or available data from various data clusters that contains pieces of data. We provide an effective framework that provides a way for effective decision making using Amazon EMR. Through various experiments done on different biological datasets, we reveal the advantages of the proposed model and present numerical results. These results indicate that the proposed framework can efficiently perform analytics over any biological datasets and obtain results in optimal time thereby maintaining the quality of the result.