Management and application of mobile big data
Abstract
Big data is the buzzword of the year. Especially in the field of telecommunications, telecom operators have been put to the test of mobile big data because it has huge, varied and complex structure with challenges to store and analysis. However, big data also has potential big value. Revealing hidden information and making further processes in mobile big data, telecom operators could depict user behaviour to improve average–revenue–per–user (ARPU) and detect real–time device information to prevent potential equipment failure. An overview of mobile big data's content, scope, methods, challenges and samples is presented in this paper. The paper also discusses the current process and analysis on mobile big data and introduces a mobile data infrastructure (MDI) and a mobile data lifetime management (MDLM) model. Finally, a mobile big data system will be described.
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Different researchers have distinct understandings towards data characteristics: some say 3Vs, others 4Vs, and 6Vs.
“Different researchers have distinct understandings towards the data characteristicssuch as some say 3Vs [volume, velocity and variety] of data (e.g.Shah, Rabhi, & Ray, 2015), others reported 4Vs [volume, velocity, variety, and variability] of data (e.g.Liao, Yin, Huang, & Sheng, 2014) and 6Vs [volume, velocity, variety, veracity, variability, and value] of data (Gandomi & Haider, 2015).”
From Critical analysis of Big Data challenges and analytical methods (2017) · cites this paper as Liao, Yin, Huang, & Sheng, 2014
Cites
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Critical analysis of Big Data challenges and analytical methods
Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017) · Journal of Business Research · doi:10.1016/j.jbusres.2016.08.001 · Cited by 2,066 · see citing works
“…some say 3Vs [volume, velocity and variety] of data (e.g. Shah, Rabhi, & Ray, 2015), others reported 4Vs [volume, velocity, variety, and variability] of data (e.g. Liao, Yin,Huang, & Sheng, 2014) and6Vs [volume, velocity, variety, veracity, variability, and value] of data (Gandomi & Haider, 2015).”
- Authors
- Liao, Z., Yin, Q., Huang, Y., & Sheng, L.
- Affiliations
- China Mobile (China) (CN)
- Huazhong University of Science and Technology (CN)
- Venue
- International Journal of Embedded Systems
- Publisher
- Inderscience Publishers
- Published
- 2015
- Volume
- 7
- Issue
- 1
- Pages
- 63
- ISSN
- 1741-1068 (ISSN-L)
- 1741-1076
- OpenAlex
- W2145272056
- Cited by
- 20
- References
- 18
- Citations by year
- 2024: 3 · 2023: 3 · 2022: 1 · 2021: 2 · 2020: 1 · 2019: 1 · 2018: 5 · 2017: 1 · 2016: 3
- FWCI
- 2.49
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Embedded Systems (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Data and IoT Technologies
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Keywords
- mobile big data, telecommunications companies, user behavior analysis, average revenue per user, equipment failure prevention
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