Mobile Big Data Analytics Using Deep Learning and Apache Spark

Abstract

The new mobile big data is the outcome of the widespread use of mobile devices like smartphones and IoT devices. Without employing suitable analytical and learning methodologies to extract crucial facts and hidden designs from the data, collecting MBDs is not economically viable. We drew on the work of other academics who published their findings between 2015 and 2021 for this analysis. This white paper gives an introduction to deep learning in MBD analysis and a straightforward training exercise, and it verifies the viability of customizable learning architectures via Apache Spark. Certain deep learning tasks, in particular, are carried out using guided iterations. Many Spark staff members have been let go. Recent progress has been made due in large part to the availability of "big data." An expert deep model is constructed by having each Spark worker train a fractional deep model on a shared MBD and then averaging the range of all Midway models. For instance, in the business world, platforms like Apache Hadoop and Apache Spark have become increasingly well-known in recent years. The importance of efficient big data analytics in solving AI-related problems is becoming more and more apparent. His Spark infrastructure now includes the multi-computational package MLlib. Although the library can be used to do many different types of AI computations, the Spark architecture is particularly well-suited to very slow and computationally costly methods like deep learning.