Improving Computational Efficiency in Personalized Healthcare Data by Implementing Sparse Matrices

Abstract

A collection of large and complex data sets which are difficult to process using common database management tools or traditional data processing applications. Big data is not just about size and also finds insights from complex, noisy, heterogeneous, longitudinal, and voluminous data. In this paper we are describing various problems that we believe need to be tackled in order to have an effective integration of big data analytics and Virtual Physiological Human (VPH) modeling in healthcare. And also in this paper we are extending our solution by implementing sparse matrix to enhance computational efficiency of big data analysis.