Management and application of mobile big data

Liao, Zhensong; Yin, Qiang; Huang, Yan; Li, Sheng

doi:10.1504/ijes.2015.066143

Cited by 23 publications

(13 citation statements)

References 13 publications

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“…Data integration is a step to integrate data from different resources with different formats and categories and to handle missing data fields [7]. Figure 2 represents the procedures of data collection and preprocessing.…”

Section: Raw Datamentioning

confidence: 99%

“…Volume is the most obvious feature of MBD. In the forthcoming 5G network and the era of MBD, conventional store and analysis methods are incapable of processing the 1000x or more wireless traffic volume [7,82]. It is of great urgency to improve present MBD analysis methods and propose new ones.…”

Section: Properties Of Mobile Big Datamentioning

confidence: 99%

“…The data is now continuously streaming into the servers in real-time and makes the original batch process break down [84]. Due to the high generating rate of MBD, velocity is the efficiency requirement of MBD analysis since real-time data processing and analysis are extremely important in order to maximize the value of MBD streams [7].…”

Section: Properties Of Mobile Big Datamentioning

confidence: 99%

“…The MBD can be divided into structured data, semistructured data, and unstructured data. Here, unstructured data are usually collected in new applications and have random data fields and contents [7]; therefore, they are difficult to analyze before data cleaning and integration.…”

Section: (3) Variety: Heterogeneous and Nonstructured Mobile Multimedmentioning

confidence: 99%

“…The MBD can be considered as a huge quantity of mobile data which are generated from a massive number of mobile devices and cannot be processed and analyzed by a single machine [6,7]. MBD is playing and will play a more important role than ever before by the popularization of mobile devices including smartphones and IoT gadgets especially in the era of 4G and the forthcoming the fifth generation (5G) [4,8].…”

Section: Introductionmentioning

confidence: 99%

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A Survey on Machine Learning‐Based Mobile Big Data Analysis: Challenges and Applications

Xie

Song

et al. 2018

Wireless Communications and Mobile Computing

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This paper attempts to identify the requirement and the development of machine learning-based mobile big data (MBD) analysis through discussing the insights of challenges in the mobile big data. Furthermore, it reviews the state-of-the-art applications of data analysis in the area of MBD. Firstly, we introduce the development of MBD. Secondly, the frequently applied data analysis methods are reviewed. Three typical applications of MBD analysis, namely, wireless channel modeling, human online and offline behavior analysis, and speech recognition in the Internet of Vehicles, are introduced, respectively. Finally, we summarize the main challenges and future development directions of mobile big data analysis.

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Section: Raw Datamentioning

confidence: 99%

Section: Properties Of Mobile Big Datamentioning

confidence: 99%

Section: Properties Of Mobile Big Datamentioning

confidence: 99%

Section: (3) Variety: Heterogeneous and Nonstructured Mobile Multimedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Survey on Machine Learning‐Based Mobile Big Data Analysis: Challenges and Applications

Xie

Song

et al. 2018

Wireless Communications and Mobile Computing

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Adapting a Multi-SOM Clustering Algorithm to Large Banking Data

Khanchouch¹,

Limam

2018

Advances in Intelligent Systems and Computing

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Industry 5.0 or industry 4.0S? Introduction to industry 4.0 and a peek into the prospective industry 5.0 technologies

Santhi¹,

Muthuswamy²

2023

Int J Interact Des Manuf

111

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The Industrial Revolution can be termed as the transformation of traditional industrial practices into new techniques dominated by the technologies available at that time. The first three industrial revolutions were driven respectively by mechanization, electrification, and automation which had gradually transformed the agrarian economy into a manufacturing-based economy. It helped in enhancing the lifestyle of the factory workers and the healthcare system, which improved the overall quality of living. The industries that adapted to the change witnessed a tremendous increase in the production of goods, competitive advantage, and cross-border business opportunities. While we are currently living to see the fourth industrial revolution (also known as Industry 4.0) unfolding around us, the world is poised for the next big leap, the fifth industrial revolution or Industry 5.0. Hence, the first half of the paper outlines the enabling technologies of Industry 4.0 and conceptualizes how they would act as the foundation for the fifth industrial revolution. The socio-economic challenges of the technologies and the need for Industry 5.0 technologies are also discussed. The second half of the paper outlines the prospective technologies of Industry 5.0, their potential applications from the perspective of industry leaders and scholars and conceptualizes how they can overcome the challenges of Industry 4.0. The definition of “sustainability trilemma” a new term coined by the authors, and the reasoning for calling the next industrial revolution “Industry 4.0S” (another new term) rather than Industry 5.0 are also presented.

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Management and application of mobile big data

Cited by 23 publications

References 13 publications

A Survey on Machine Learning‐Based Mobile Big Data Analysis: Challenges and Applications

A Survey on Machine Learning‐Based Mobile Big Data Analysis: Challenges and Applications

Adapting a Multi-SOM Clustering Algorithm to Large Banking Data

Industry 5.0 or industry 4.0S? Introduction to industry 4.0 and a peek into the prospective industry 5.0 technologies

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