Enterprise human resource management platform based on FPGA and data mining

2020 ◽  
pp. 103330 ◽  
Author(s):  
Ping Liu ◽  
Wang Qingqing ◽  
Wentao Liu
Keyword(s):  
Data Mining ◽  
Resource Management ◽  
Human Resource Management ◽  
Human Resource ◽  
Management Platform

Data Warehouse and Business Intelligence Systems in the Context of E-HRM

2009 ◽  
pp. 223-229
Author(s):  
Martin Burgard ◽  
Franca Piazza
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Resource Management ◽  
Human Resource Management ◽  
Data Warehouse ◽  
Human Resource ◽  
Business Intelligence ◽  
Multidimensional Data ◽  
Business Intelligence Systems ◽  
Analytical Processing

The increased use of information technology leads to the generation of huge amounts of data which have to be stored and analyzed by appropriate systems. Data warehouse systems allow the storage of these data in a special multidimensional data base. Based on a data warehouse, business intelligence systems provide different analysis methods such as online analytical processing (OLAP) and data mining to analyze these data. Although these systems are already widely used and the usage is still growing, their application in the area of electronic human resource management (e-HRM) is rather scarce. Therefore, the objective of this article is to depict the components and functionality of these systems and to illustrate the application possibilities and benefits of these systems by selected application examples in the context of e-HRM.

A Proposed Model for Predicting Employees’ Performance Using Data Mining Techniques

2020 ◽  
Vol 17 (8) ◽  
pp. 3804-3809
Author(s):  
A. Yovan Felix ◽  
Karthik Reddy Vuyyuru ◽  
Viswas Puli
Keyword(s):  
Data Mining ◽  
Resource Management ◽  
Human Resource Management ◽  
Human Resource ◽  
Human Resource Professionals ◽  
Quality Factors ◽  
Proposed Model ◽  
Opportune Time ◽  
Using Data ◽  
The Impact

Human Resource Management has gotten one of the basic pastimes of supervisors and chiefs in practically wide variety of corporations to include plans for accurately locating profoundly qualified representatives. In similar way, administrations come to be intrigued about the presentation of these representatives. Particularly to guarantee the fitting person apportioned to the beneficial employment on the opportune time. From right here the enthusiasm of statistics in mining process has been growing that its goal is disclosure of facts from huge measures of statistics. Three fundamental Data Mining strategies were applied for building the arrangement version and distinguishing the quality factors that emphatically impact the exhibition. To get a profoundly actual version, a few trials were achieved dependent on the beyond procedures which can be actualized in WEKA tool for empowering leaders and Human Resource professionals to anticipate and improve the exhibition of their representatives. This paper makes use of Hadoop for the remedy of great measure of data with which may be guaranteed to be able to decide the impact.

scholarly journals Influence of data mining technology in information analysis of human resource management on macroscopic economic management

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0251483
Author(s):  
Ai Zhang
Keyword(s):  
Data Mining ◽  
Resource Management ◽  
Decision Tree ◽  
Human Resource Management ◽  
Human Resource ◽  
Classification Accuracy ◽  
Ensemble Classifier ◽  
Economic Management ◽  
Mining Technology ◽  
Tree Algorithms

The purposes are to manage human resource data better and explore the association between Human Resource Management (HRM), data mining, and economic management. An Ensemble Classifier-Decision Tree (EC-DT) algorithm is proposed based on the single decision tree algorithm to analyze HRM data. The involved single decision tree algorithms include C4.5, Random Tree, J48, and SimpleCart. Then, an HRM system is established based on the designed algorithm, and the evaluation management and talent recommendation modules are tested. Finally, the designed algorithm is compared and tested. Experimental results suggest that C4.5 provides the highest classification accuracy among the single decision tree algorithms, reaching 76.69%; in contrast, the designed EC-DT algorithm can provide a classification accuracy of 79.97%. The proposed EC-DT algorithm is compared with the Content-based Recommendation Method (CRM) and the Collaborative Filtering Recommendation Method (CFRM), revealing that its Data Mining Recommendation Method (DMRM) can provide the highest accuracy and recall, reaching 35.2% and 41.6%, respectively. Therefore, the data mining-based HRM system can promote and guide enterprises to develop according to quantitative evaluation results. The above results can provide a reference for studying HRM systems based on data mining technology.

Data Warehouse and Business Intelligence Systems in the Context of E-HRM

2011 ◽  
pp. 1013-1020
Author(s):  
Martin Burgard ◽  
Franca Piazza
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Resource Management ◽  
Human Resource Management ◽  
Data Warehouse ◽  
Human Resource ◽  
Business Intelligence ◽  
Multidimensional Data ◽  
Business Intelligence Systems ◽  
Analytical Processing

The increased use of information technology leads to the generation of huge amounts of data which have to be stored and analyzed by appropriate systems. Data warehouse systems allow the storage of these data in a special multidimensional data base. Based on a data warehouse, business intelligence systems provide different analysis methods such as online analytical processing (OLAP) and data mining to analyze these data. Although these systems are already widely used and the usage is still growing, their application in the area of electronic human resource management (e-HRM) is rather scarce. Therefore, the objective of this article is to depict the components and functionality of these systems and to illustrate the application possibilities and benefits of these systems by selected application examples in the context of e-HRM.

scholarly journals Use of Data Mining Techniques in Human Resource Management

2017 ◽  
Vol 1 (1) ◽  
pp. 41-46
Author(s):  
Okaile R. Marumo ◽  
Tumisang Angela Mmopelwa
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Resource Management ◽  
Human Resource Management ◽  
Human Resource ◽  
Transformational Change ◽  
Decision Making Process ◽  
Data Mining Techniques ◽  
The Past ◽  
Use Of Data

In the past few years, Analytics has rapidly risen in among organizations within the field of human resource management. To the present date, however, Human Resource Analytics has not been subject to a lot of scrutiny from educational researchers. The aim of this paper is so to look at Different Mining Techniques could be implemented in the HR Department to enhance or support their decision making process. This will improve existing practices of HR analytics and will deliver transformational change indeed

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