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Enterprise Management Optimization by Using Artificial Intelligence and Edge Computing

Enterprise Management Optimization by Using Artificial Intelligence and Edge Computing

Shanshan Wang
Copyright: © 2022 |Volume: 13 |Issue: 3 |Pages: 9
ISSN: 1947-3532|EISSN: 1947-3540|EISBN13: 9781683181835|DOI: 10.4018/IJDST.307994
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MLA

Wang, Shanshan. "Enterprise Management Optimization by Using Artificial Intelligence and Edge Computing." IJDST vol.13, no.3 2022: pp.1-9. http://doi.org/10.4018/IJDST.307994

APA

Wang, S. (2022). Enterprise Management Optimization by Using Artificial Intelligence and Edge Computing. International Journal of Distributed Systems and Technologies (IJDST), 13(3), 1-9. http://doi.org/10.4018/IJDST.307994

Chicago

Wang, Shanshan. "Enterprise Management Optimization by Using Artificial Intelligence and Edge Computing," International Journal of Distributed Systems and Technologies (IJDST) 13, no.3: 1-9. http://doi.org/10.4018/IJDST.307994

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Abstract

In the internet era, huge data is generated every day. With the help of cloud computing, enterprises can store and analyze these data more conveniently. With the emergence of the internet of things, more hardware devices have accessed the network and produced massive data. The data heavily relies on cloud computing for centralized data processing and analysis. However, the rapid growth of data volume has exceeded the network throughput capacity of cloud computing. By deploying computing nodes at the edge of the local network, edge computing allows devices to complete data collection and preprocessing in the local network. Thus, it can overcome the problems of low efficiency and large transmission delay of cloud computing for massive native data. This paper designs a human trajectory training system for enterprise management. The simulation demonstrates that the system can support human trajectory tracing and prediction for enterprise management.

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