The proliferation of mobile devices, such as smartphones and Internet of Things gadgets, has resulted in the recent mobile big data era. Collecting mobile big data is unprofitable unless suitable analytics and learning methods are utilized to extract meaningful information and hidden patterns from data. This article presents an overview and brief tutorial on deep learning in mobile big data analytics and discusses a scalable learning framework over Apache Spark. Specifically, 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 mobile, 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.
Mobile communication, Machine learning, Computational modeling, Mobile handsets, Sparks, Big data, Sensors
Software and Cyber-Physical Systems
Institute of Electrical and Electronics Engineers (IEEE)
ALSHEIKH, Mohammad Abu; NIYATO, Dusit; LIN, Shaowei; Hwee-Pink TAN; and HAN, Zhu.
Mobile big data analytics using deep learning and apache spark. (2016). IEEE Network. 30, (3), 22-29. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3422
Copyright Owner and License
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.