Mobile Big Data Analytics Using Deep Learning and Apache Spark

Abstract

The new mobile big data is the result of the proliferation of mobile devices such as PDAs and Internet of Things (IoT) gadgets. Collecting MBDs is not economically viable unless appropriate analytical and learning approaches are applied to extract key facts and hidden designs from the data. In the current study we have used published data of different researchers from 2015 to 2021. This white paper validates flexible learning structures via Apache Spark and provides an introduction to deep learning in MBD analysis and a simple training exercise. In particular, guided iterations are used to perform certain deep learning tasks. We have reduced the number of many Spark employees. With the prevalence of big data, there have been some recent advances in this area. Each Spark worker trains a fractional deep model on some common MBD and averages the range of all Midway models to build an expert deep model. For example, systems such as Apache Hadoop and Apache Spark have grown in popularity in recent years and are fairly well known, especially in the commercial world. It is becoming increasingly clear that effective big data analytics are essential to address issues related to artificial intelligence. As such, MLlib, a multi-computational library, has been implemented in his Spark system. The library supports a wide variety of AI computations, but the Spark setup can be effectively used to do very slow and computationally intensive approaches like deep learning.