Advancement Of Deep Learning In Big Data And Distributed Systems

Digital computing space has grown dramatically since the beginning of the 2000s to deal with an increase in data proliferation. These come from a wide variety area. For example, the number of connected devices explodes with the advent of the Internet of Things. These machines generate a growing number of data, which must be analyzed, by their interactions with the outside environment and its various sensors. Social networks are also another field in which various data has been used, interactive data and metadata that provide information on user profiles. All these data require the storage of large capacity and analysis of several data. In effect, if the arrival of this quantities of information demanded storage improvement, significant advances in processing and interpretation were also required and feasible. In this paper, the main contributions are summarized in a comparison table as detailed in Table 1, like the objectives, challenges, and novelty of each paper are clarified. The architecture or model and applications used- finally, the recommendations for each.