Semi-supervised human resource scheduling based on deep presentation in the cloud

Lin, Yuanmo; Wang, Xinmei; Xu, Rongbin

doi:10.1186/s13638-020-01677-6

Cited by 11 publications

(9 citation statements)

References 28 publications

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“…In recent years, China's informatization construction has made great progress, and both industries and enterprises have felt various benefits from the application of information technology. The WAP business is also growing at a rapid pace [2], in the context of the deepening of China's reform and opening up and the domestic and international environment of economic globalization. Like all developing enterprises, the problem faced by tobacco enterprise informatization is a problem in development and improvement, and all solutions point to the data center system based on data warehouse.…”

Section: Introductionmentioning

confidence: 99%

The Implementation of Unified Application Data Collection and Analysis in Tobacco Enterprise Platform Design

Jin

Lou

2022

Journal of Function Spaces

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With the continuous deepening and development of tobacco manufacturing informatization construction, tobacco manufacturing enterprises have the characteristics of strong production capacity, high degree of production automation, and advanced enterprise-level management information system. In order to cooperate with the market and brand strategy of the tobacco industry, the State Tobacco Monopoly Administration has carried out serious strategic thinking on informatization and customized the overall plan and strategic goals. In recent years, China’s informatization construction has made great progress, and both industries and enterprises have felt various benefits from the application of information technology. In the deployment and development of mobile services, it is often local and municipal mobile companies that, in order to meet the different needs of the market, immediately carry out corresponding services, resulting in one service for each municipal subsidiary in the same province or one platform for a class of services. Situation is good for quickly meeting user needs at that time. The exchange and communication of enterprise personnel information are becoming more and more frequent, and the collection of enterprise information through mobile has become a new way of enterprise information collection. The work orders, process parameters, documents, notifications, and instructions generated by the planning layer are sent to the production control layer to respond, guide, and trigger the production site events. The ultimate goal of mobile data collection is to gather the collected data into the enterprise production data center, which plays an important role in mobile devices, mobile performance, mobile planning, mobile balancing, mobile prediction, and so on. Based on the unified mobile application data collection method, this paper analyzed and analyzed the design of application architecture in the tobacco enterprise platform and further described the business functions, security performance, and system characteristics of the platform.

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Section: Introductionmentioning

confidence: 99%

The Implementation of Unified Application Data Collection and Analysis in Tobacco Enterprise Platform Design

Jin

Lou

2022

Journal of Function Spaces

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Section: Resultsmentioning

confidence: 99%

“…Also associated with the concept of human resource virtualization, platforms have been designed to share talent in activities related to the collaborative economy, i.e. cloud platforms specialized in crowdsourcing (Xu et al , 2015; Liang et al , 2020; Lin et al , 2020).…”

Section: Resultsmentioning

confidence: 99%

“…An innovative application with an excellent future is the implementation of cloud platforms to monitor and analyze human resources through the use of wearable sensors (Vega-Barbas et al, 2019) with very diverse uses, for example, an ergonomic analysis for preventive purposes (Vega-Barbas et al, 2019). Another application enables the location of human resources using algorithms based on neural networks (Lin et al, 2020). Thus, thanks to the enormous computing and storage power of cloud computing and its easy access from anywhere, it is possible to apply new technologies that are revolutionizing HRM, such as artificial intelligence and big data.…”

Section: Cloud Computing and Human Resource Managementmentioning

confidence: 99%

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Cloud computing and human resource management: systematic literature review and future research agenda

Marín

Dias

Navimipour

et al. 2021

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PurposeThis study aims to provide an overview of what characterizes the current state of research in the field of cloud computing use in human resource management (HRM) with the identification, analysis and classification of the existing literature and lines of research addressed and to provide guidance for future research.Design/methodology/approachThe systematic literature review (SLR) technique has been used to identify, select, analyze and evaluate the existing publications on cloud computing and HRM. A total of 35 papers published up to December 2020 have been obtained from the Web of Science (WoS) scientific database. The research design has allowed us to determine what characterizes the current state of research on the use of cloud computing in HRM and obtain a novel classification of the literature that identifies four lines of research and the contributions in each line and has allowed us to define the future research agenda.FindingsThe four groups into which the papers on the cloud computing-HRM relationship have been classified are: (1) studies focused on the development of cloud platforms for HRM that highlight technical aspects, (2) papers that focus on the concept of human resource elasticity, (3) papers on the adoption and/or implantation of cloud platforms for HRM and (4) studies that highlight the effects or implications of cloud platforms for HRM. This paper proposes some new opportunities for future research and presents some helpful implications from the theoretical and management perspectives.Research limitations/implicationsThis study uses only scientific articles in the WoS database with a Journal Citation Report (JCR) or SCImago Journal Rank (SJR) impact.Originality/valueThis paper provides an overview of the knowledge on cloud computing and HRM research and offers recommendations for future research.

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“…Many researchers have also used neural network technology to fully study the relevant factors in human resource management, and it has also achieved good research results. Lin et al [21] believed that the traditional human resource model can no longer meet the development model and requirements of today enterprises. In order to improve human resource management and job matching, they used an autoencoder neural network method and cloud computing to study the relationship between human resources and job matching.…”

Section: Related Workmentioning

confidence: 99%

Human Resource Decision-Making and Recommendation Based on Hadoop Distributed Big Data Platform

Chen

2022

Mathematical Problems in Engineering

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The innovative development of an enterprise is a necessary factor for its long-term development, and science and technology is a necessary driving force for its development. An effective management of human resources management will find more high-quality talent, and it will also be efficient management of the enterprise staff. The efficiency of the traditional human resource management is relatively low; the approach will produce larger errors for the enterprise. This study combines the Hadoop distributed platform and big data technology to conduct a decision recommendation research on the relevant factors of human resource management. In this study, the decision tree is used to classify the data related to human resource management. The data feature of human resource will be predicted by the neural network method, and human resource managers will refer to the prediction data from the output layer of neural network. Human resource managers will make corresponding decisions and recommendations. The research results show that Hadoop technology and big data technology have better accuracy in human resource management recommendation system, which contains ConvLSTM method and decision tree method in this study. The maximum prediction error of the ConvLSTM algorithm in predicting the relevant factors of the human resource decision-making system is only 2.67%, and this part of the error comes from the employee-job matching index. The smallest forecast is only 1.98%.

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Semi-supervised human resource scheduling based on deep presentation in the cloud

Cited by 11 publications

References 28 publications

The Implementation of Unified Application Data Collection and Analysis in Tobacco Enterprise Platform Design

The Implementation of Unified Application Data Collection and Analysis in Tobacco Enterprise Platform Design

Cloud computing and human resource management: systematic literature review and future research agenda

Human Resource Decision-Making and Recommendation Based on Hadoop Distributed Big Data Platform

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