2016/02/23 by Mohammad Abu Alsheikh, Dusit Niyato, Shaowei Lin +2 · 275 citations
Computer Science · #Analytics #Artificial intelligence #Big data #Computer science #Context (archaeology) #Context-Aware Activity Recognition Systems #Data Stream Mining Techniques #Data mining #Data science #Database #Deep learning #IoT and Edge/Fog Computing #Machine learning #Mobile computing #Mobile device #Operating system #SPARK (programming language) #Scalability #Speedup #World Wide Web #cs.DC #cs.LG #cs.NE
paper · pdf · doi:10.1109/mnet.2016.7474340
published in IEEE Network 30(3), 22-29 (Institute of Electrical and Electronics Engineers)
arxiv created 2016/02/23 · openalex publication_date 2016/05/01 · openalex created_date 2016/06/24 · arxiv updated 2016/08/16 · openalex updated_date 2026/08/05
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.