Abstract

Big Data deals with large-volume, complex, growing data sets with multiple, autonomous sources. With the fast\ndevelopment of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science\nand engineering domains, including physical, biological and biomedical sciences. The Big Data challenges are broad in the\ncase of accessing, storing, searching, sharing, and transfer. Managing Big Data is not easy by using traditional relational\ndatabase management systems; it requires instead parallel computing of dataset. This work makes use of a HACE theorem\nthat characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining\nperspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user\ninterest modeling, and security and privacy considerations. In this work the challenging issues in the data-driven model and\nalso in the Big Data revolution are analyzed.