Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/84039
Title: Mobile big data analytics using deep learning and Apache Spark
Authors: Niyato, Dusit
Abu Alsheikh, Mohammad
Lin, Shaowei
Tan, Hwee-Pink
Han, Zhu
Keywords: Distributed deep learning
Big data
Issue Date: 2016
Source: Abu Alsheikh, M., Niyato, D., Lin, S., Tan, H.-P., & Han, Z. (2016). Mobile big data analytics using deep learning and Apache Spark. IEEE Network, 30(3), 22-29.
Series/Report no.: IEEE Network
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, the learning of deep models 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.
URI: https://hdl.handle.net/10356/84039
http://hdl.handle.net/10220/41585
ISSN: 0890-8044
DOI: 10.1109/MNET.2016.7474340
Rights: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/MNET.2016.7474340].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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