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Innovative Knowledge Automation Framework in DM and Collaborative Edge Computing Social IoT Systems

Innovative Knowledge Automation Framework in DM and Collaborative Edge Computing Social IoT Systems

Qiansha Zhang, Gang Li
Copyright: © 2022 |Volume: 13 |Issue: 7 |Pages: 23
ISSN: 1947-3532|EISSN: 1947-3540|EISBN13: 9781668474082|DOI: 10.4018/IJDST.307953
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MLA

Zhang, Qiansha, and Gang Li. "Innovative Knowledge Automation Framework in DM and Collaborative Edge Computing Social IoT Systems." IJDST vol.13, no.7 2022: pp.1-23. http://doi.org/10.4018/IJDST.307953

APA

Zhang, Q. & Li, G. (2022). Innovative Knowledge Automation Framework in DM and Collaborative Edge Computing Social IoT Systems. International Journal of Distributed Systems and Technologies (IJDST), 13(7), 1-23. http://doi.org/10.4018/IJDST.307953

Chicago

Zhang, Qiansha, and Gang Li. "Innovative Knowledge Automation Framework in DM and Collaborative Edge Computing Social IoT Systems," International Journal of Distributed Systems and Technologies (IJDST) 13, no.7: 1-23. http://doi.org/10.4018/IJDST.307953

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Abstract

Digital marketing-based innovative knowledge management helps people inspire creativity and cultural changes required to advance the organization and satisfy changing business requirements. Knowledge workers can respond more rapidly when they have quicker access to resources and information across the company. A knowledge-based approach views innovation as a process characterized by the knowledge needed to understand how the innovation was created. The term “digital marketing automation” (DMA) refers to software platforms and technologies built for marketing departments and enterprises to sell online and automate tedious tasks more effectively. Digital marketing encompasses all forms of advertising that take place online, including but not limited to websites, search engines, social media, email, and mobile apps. An entirely new approach to big data processing has emerged because of the rise of edge computing in the internet of things environment. As a result of these findings, a distributed neural network cloud-edge computing paradigm is presented.

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