The mobile network is at a critical tipping point. On the one hand, it is heavily stressed by the explosive increase of traffic, and the steep transition from a smartphone-dominated market to one that is dazzling diverse, all subject to the same electromagnetic theory-ruled physical world. On the other hand, it is hopefully excited by the rapid development of data science, more powerful and versatile computing, and increasingly affordable hardware. It seems that we have come to the point at which in order to make the ends meet, one would need to put all the pieces together. In this paper, we aspire to establish a holistic framework for mobile big data (MBD) based network intelligence. Combining the top-down and bottom-up approaches, we put in context the recent development in the mobile network architecture, resource-based management theory, MBD orchestration, and data analytics, and establish a hierarchical network intelligence architecture fueled by MBD analytics. This architecture nurtures holistic understanding on how the multidimensional, multilateral, and multigranular MBD, together with its processing, can be plugged into the network architecture.
Bibliographical noteFunding Information:
Manuscript received February 2, 2018; revised June 26, 2018; accepted July 22, 2018. Date of publication August 7, 2018; date of current version January 16, 2019. This work was supported in part by the National Natural Science Foundation of China under Grant 61622101 and Grant 61571020, in part by the Shenzhen Fundamental Research Fund under Grant JCYJ20170411102217994, and in part by the Shenzhen Peacock Plan under Grant KQTD2015033114415450. (Corresponding author: Xiang Cheng.) X. Cheng is with the State Key Laboratory of Advanced Optical Communication Systems and Network, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China, and also with the Shenzhen Research Institute of Big Data, Shenzhen 518172, China (e-mail: firstname.lastname@example.org).
© 2014 IEEE.
- Big data applications
- Data analysis
- Data collection
- Machine learning
- Mobile communication