RETRACTED ARTICLE: Data cleansing method of talent management data in wireless sensor network based on data mining technology

Bai, Yanli

doi:10.1186/s13638-019-1342-3

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…By constructing relevant indicators of policy attractiveness, the factor analysis method is used to measure the objective attractiveness and absolute attractiveness of policies, and the evaluation results of high-level talent attractiveness of provinces at the policy level are obtained [21,22]. Based on the content analysis method, some studies examine the development policy of scientific and technological innovation talents from the perspective of the development process of scientific and technological innovation talents [23,24,25]. According to a questionnaire survey concerning the introduction of overseas talents by universities, some research proposes countermeasures and suggestions for improving the market-oriented system and mechanism of introduction of overseas high-level talents in cities from six aspects [26].…”

Section: Introductionmentioning

confidence: 99%

Understanding regional talent attraction and its influencing factors in China: From the perspective of spatiotemporal pattern evolution

Liu

Zhang

et al. 2020

PLoS ONE

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Talents are not only an important strategic resource for promoting regional development but also a core element for maintaining competitiveness. We organize the evaluation index system of regional talent attraction into the following four aspects: regional development, industry development, income and regional environment. Combined with the talent possession of 31 provinces (cities) from 2010 to 2018, we establish a regression equation of the relationship between the evaluation index and talent possession by using a stepwise regression and the Bayesian prior function. Simultaneously, we apply the spatial autocorrelation analysis method to measure the correlation and agglomeration degree of the talent attraction level of provinces and municipalities in China. The results reveal the following. (1) From 2010 to 2018, the talent attractiveness level of China's provinces shows a steady upward trend with an average annual growth rate of 5.804%. The regional environment has the highest score, and the income level has the lowest score. (2) The level of talent attraction in China shows a decreasing trend from east to west, and the ranking in 2018 was "East Coast > North Coast > Southern Coast > Middle Yangtze River > Middle Yellow River > Southwest > Northeast > Greater Northwest". The trend of spatial agglomeration is apparent and gradually increases over the years. The numbers of hot and cold spots are relatively large and concentrated in the eastern coast and western region, respectively. (3) The level of economic development, quality of people's life, and level of the development of the tertiary industry have a great impact on the attractiveness of talents. Talents also pay more attention to regional medical, education and transportation indicators. These research results can provide some guidance and references for the formulation of talent introduction policies in various provinces and municipalities.

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Section: Introductionmentioning

confidence: 99%

Understanding regional talent attraction and its influencing factors in China: From the perspective of spatiotemporal pattern evolution

Liu

Zhang

et al. 2020

PLoS ONE

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“…It does not need professional skills to operate the system and process the data. It has strong interactive performance and good research significance, It is of great significance to industrial production [16].…”

Section: B Nonfunctional Requirements Analysismentioning

confidence: 99%

Research on Oil Well Data Cleaning System

Feng

Zhao

2022

International Journal of Advanced Network, Monitoring and Controls

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In the information age, with the continuous development of Internet technology, information data occupies every field of contemporary society. The development of the big data age makes these data more and more prominent. While users read the information they need from these massive data, data quality has also become a concern of users. A large number of data are preprocessed before data analysis, such as some duplicate values, missing values deal with inaccurate and other abnormal data, and filter the data through the data cleaning system to improve the standardization of the data, so as to improve the analysis efficiency of the data, reduce some unnecessary expenses, and save time and effort. The data cleaning system in this paper is implemented based on flash framework. Taking Python as the main language for data cleaning, technical cleaning and standard integration are carried out for some structural problems, duplication problems and missing problems of some different source data. Through the processing of abnormal data, the data quality and data analysis efficiency are greatly improved.

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Data mining technology for mechanical engineering computer test system

2020

Mechanical Systems and Signal Processing

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RETRACTED ARTICLE: Data cleansing method of talent management data in wireless sensor network based on data mining technology

Cited by 4 publications

References 11 publications

Understanding regional talent attraction and its influencing factors in China: From the perspective of spatiotemporal pattern evolution

Understanding regional talent attraction and its influencing factors in China: From the perspective of spatiotemporal pattern evolution

Research on Oil Well Data Cleaning System

Data mining technology for mechanical engineering computer test system

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