Mobile big data analytics using deep learning and Apache Spark

Mobile big data analytics using deep learning and Apache Spark
Title:
Mobile big data analytics using deep learning and Apache Spark
Other Titles:
IEEE Network
DOI:
10.1109/MNET.2016.7474340
Publication Date:
20 May 2016
Citation:
M. Abu Alsheikh, D. Niyato, S. Lin, H. p. Tan and Z. Han, "Mobile big data analytics using deep learning and Apache Spark," in IEEE Network, vol. 30, no. 3, pp. 22-29, May-June 2016
Abstract:
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.
License type:
PublisherCopyrights
Funding Info:
Description:
(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
ISSN:
0890-8044
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