Sage Journals: Discover world-class research

Abstract

In order to promote the standardized, scientific and intelligent of listed companies needs to be optimized in combination with cloud computing. This requires us to conduct real-time and reasonable management of the big data involved in corporate human resources and build a suitable information platform for management and control. Therefore, using cloud computing, this study systematically explores big data management in the human resource management of rail transit enterprises. This study mainly analyzes the two main factors of big data management involved in rail transit human resources. This paper mainly introduces three typical cloud computing methods. In addition, the mathematical fitting results show that human resource management involves a good linear function relationship between the corresponding predicted values of two important indicators. This study highlights the significance of leveraging cloud computing and big data management to enhance the standardization, scientific precision, and intelligent optimization of human resource management in rail transit enterprises.

Keywords

cloud computing technology listed company big data human resource management information platform

Get full access to this article

View all access options for this article.

References

Amin

Choi

. Hotspots analysis using cyber-physical-social system for a smart city. IEEE Access 2020; 8: 122197–122209.

Yang

Liu

, et al. Brief introduction of medical database and data mining technology in big data era. J Evid Base Med 2020; 13(1): 57–69.

Baggs

de Bettignies

Ries

. Product market competition and returns to talent. Economics Manag Strategy, 2019, 22(3): 569–593.

. Money held for moving stars: talent competition and corporate cash holdings. J Corp Finance 2019; 51: 210–234.

Gao

Rai

. Local demand-pull policy and energy innovation: evidence from the solar photovoltaic market in China. Energy Pol 2019; 128: 364–376.

Bekkerman

Brester

Tonsor

. An alternative approach to measuring demand changes in meat markets. Int Food Agribus Manag Rev 2019; 22(3): 397–412.

Zhang

, et al. Global digital compact: a mechanism for the governance of online discriminatiory and misleading content generation. Int J Hum Comput Interact 2024; 2(3): 1–28.

Choi

Wallace

Wang

. Big data analytics in operations management. Prod Oper Manag 2019; 27(10): 1868–1883.

Krivulya

. The specificity of development and the formation of the peculiarities of the Russian animational industry in the context of the conception of childhood. Nauka Televideniya-The Art and Science of Television 2020; 14(2): 107–135.

10.

Palu

Ranon

, et al. Thinking like a director: film editing patterns for virtual cinematographic storytelling. ACM Trans Multimedia Comput Commun Appl 2020; 14(4): 1–22.

11.

Strohmeier

. Digital human resource management: a conceptual clarification. German Journal of Human Resource Management-Zeitschrift Fur Personalforschung 2020; 34(3): 345–365.

12.

Hoon

Hack

Kellermanns

. Advancing knowledge on human resource management in family firms: an introduction and integrative framework. German Journal of Human Resource Management-Zeitschrift Fur Personalforschung 2019; 33(3): 147–166.

13.

Zhao

Xue

Niu

. Enterprise human resource management index based on fuzzy system. J Intell Fuzzy Syst 2021; 40(2): 3137–3146.

14.

LX.

A resource management method based on organizational behavior theory and hidden markov algorithm. Cluster Comput 2019; 22(2): S4941–S4948.

15.

Zhang

Wang

Cao

, et al. Knowledge management component in managing human resources for enterprises. Inf Technol Manag 2022; 13(4): 341–349.

16.

Wang

Bai

Zhao

, et al. The removal method of the blasting vibration signal trend item and noise. Shock Vib 2021; 2021: 1–10.

17.

Zhao

Shan

Wang

. Research on vibration effect of tunnel blasting based on an improved Hilbert–Huang transform. Environ Earth Sci 2021; 80(5): 206.

18.

Zhou

Chen

Zhu

. Regularization incremental Extreme learning machine with random reduced kernel for regression. Neurocomputing 2018; 321: 72–81.

19.

Tang

Chen

. A self-adaptive evolutionary weighted Extreme learning machine for binary imbalance learning. Prog Artif Intell 2018; 7(2): 95–118.

20.

Arthur

Temeng

Ziggah

. Multivariate adaptive regression Splines (MARS) approach to blast-induced ground vibration prediction. Int J Min Reclamat Environ 2020; 34(3): 198–222.

21.

Zhang

Wang

Sun

, et al. A constrained optimization method based on BP neural network. Neural Comput Appl 2018; 29(2): 413–421.

22.

Wang

Zha

. A research on the optimal design of BP neural network based on improved GEP. Int J Pattern Recogn Artif Intell 2019; 33(3): 1959007.

23.

Garcez

Gori

Lamb

, et al. Neural-symbolic computing: an effective methodology for principled integration of machine learning and reasoning. Journal of Applied Logics-Ifcolog Journal of Logics and their Applications 2019; 6(4): 611–631.

24.

Lam

Xiang

, et al. Causal cortical network for arithmetic problem-solving represents brain’s planning rather than reasoning. Int J Biol Sci 2019; 15(6): 1148–1160.

Human resources big data management platform for listed companies through cloud computing technology

Abstract

Keywords

Get full access to this article

References