Mobile Big Data Analytics Using Deep Learning and Apache Spark
This work addresses the need for efficient analytics in mobile big data for applications like activity recognition, but it is incremental as it combines existing deep learning with distributed computing.
The paper tackles the challenge of analyzing mobile big data by proposing a scalable deep learning framework using Apache Spark, which speeds up training by distributing computations across workers and averaging model parameters, achieving validated speedup on a real-world activity recognition dataset with millions of samples.
The proliferation of mobile devices, such as smartphones and Internet of Things (IoT) gadgets, results in the recent mobile big data (MBD) era. Collecting MBD is unprofitable unless suitable analytics and learning methods are utilized for extracting meaningful information and hidden patterns from data. This article presents an overview and brief tutorial of deep learning in MBD analytics and discusses a scalable learning framework over Apache Spark. Specifically, a distributed deep learning is executed as an iterative MapReduce computing on many Spark workers. Each Spark worker learns a partial deep model on a partition of the overall MBD, and a master deep model is then built by averaging the parameters of all partial models. This Spark-based framework speeds up the learning of deep models consisting of many hidden layers and millions of parameters. We use a context-aware activity recognition application with a real-world dataset containing millions of samples to validate our framework and assess its speedup effectiveness.