Towards a model-driven approach for big data analytics in the genomics Field

Abstract

The use of techniques such as Next Generation Sequencing has allowed a fast increase in data generation due to the reduction of processing costs. What at the beginning seemed to be an important step forward for the development of new approaches such as Precision Medicine, turned into an exponential growth of data that currently challenges healthcare professionals and researchers. Since the problems derived from the storage and management of vast amounts of heterogeneous data are well-known for the Big Data and Information Systems communities, the application of this knowledge to the genomic data domain can help to improve the management of the data, reduce the bottlenecks, and reveal new insights on the causes of human disease. In this way, this work is focused on the problem of data storage by proposing a Big Data architecture supported by a model-driven approach to ensure an efficient and dynamic storage of genomic data. The proposed architecture has been designed considering the main requirements for an efficient data integration and for supporting data analysis tasks.