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The Architecture of Mass Customization-Social Internet of Things System: Current Research Profile

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School of Management, Guangzhou University, Guangzhou 510000, China
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Research Center for High Quality Development of Modern Industry, Guangzhou University, Guangzhou 510000, China
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Algorithm Research Center, Joyy Inc., Guangzhou 510000, China
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Department of Building Surveying, Faculty of Built Environment, University of Malaya, Kuala Lumpur 50603, Malaysia
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Department of Engineering, The University of Hong Kong, Hong Kong 999077, China
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Warwick Business School, The University of Warwick, Coventry CV4 7AL, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Fernando Terroso-Sáenz, Andrés Muñoz and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2021, 10(10), 653; https://doi.org/10.3390/ijgi10100653
Received: 16 July 2021 / Revised: 25 September 2021 / Accepted: 27 September 2021 / Published: 28 September 2021
(This article belongs to the Special Issue Intelligent Systems Based on Open and Crowdsourced Location Data)
In the era of big data, mass customization (MC) systems are faced with the complexities associated with information explosion and management control. Thus, it has become necessary to integrate the mass customization system and Social Internet of Things, in order to effectively connecting customers with enterprises. We should not only allow customers to participate in MC production throughout the whole process, but also allow enterprises to control all links throughout the whole information system. To gain a better understanding, this paper first describes the architecture of the proposed system from organizational and technological perspectives. Then, based on the nature of the Social Internet of Things, the main technological application of the mass customization–Social Internet of Things (MC–SIOT) system is introduced in detail. On this basis, the key problems faced by the mass customization–Social Internet of Things system are listed. Our findings are as follows: (1) MC–SIOT can realize convenient information queries and clearly understand the user’s intentions; (2) the system can predict the changing relationships among different technical fields and help enterprise R&D personnel to find technical knowledge; and (3) it can interconnect deep learning technology and digital twin technology to better maintain the operational state of the system. However, there exist some challenges relating to data management, knowledge discovery, and human–computer interaction, such as data quality management, few data samples, a lack of dynamic learning, labor consumption, and task scheduling. Therefore, we put forward possible improvements to be assessed, as well as privacy issues and emotional interactions to be further discussed, in future research. Finally, we illustrate the behavior and evolutionary mechanism of this system, both qualitatively and quantitatively. This provides some idea of how to address the current issues pertaining to mass customization systems. View Full-Text
Keywords: big data; mass customization; technology application; intelligent system big data; mass customization; technology application; intelligent system
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MDPI and ACS Style

Dou, Z.; Sun, Y.; Wu, Z.; Wang, T.; Fan, S.; Zhang, Y. The Architecture of Mass Customization-Social Internet of Things System: Current Research Profile. ISPRS Int. J. Geo-Inf. 2021, 10, 653. https://doi.org/10.3390/ijgi10100653

AMA Style

Dou Z, Sun Y, Wu Z, Wang T, Fan S, Zhang Y. The Architecture of Mass Customization-Social Internet of Things System: Current Research Profile. ISPRS International Journal of Geo-Information. 2021; 10(10):653. https://doi.org/10.3390/ijgi10100653

Chicago/Turabian Style

Dou, Zixin, Yanming Sun, Zhidong Wu, Tao Wang, Shiqi Fan, and Yuxuan Zhang. 2021. "The Architecture of Mass Customization-Social Internet of Things System: Current Research Profile" ISPRS International Journal of Geo-Information 10, no. 10: 653. https://doi.org/10.3390/ijgi10100653

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