Digital Transformation of Enterprise Human Resource Management Enabled by Big Data

With the continuous development of big data technology,the digital transformation of enterprise human resource management has become a development trend.Human resources is one of the most important resources of enterprises,which is crucial to the competitiveness of enterprises.Enterprises need to attract,retain,and motivate excellent employees,thereby enhancing the innovation ability of enterprises and improving competitiveness and market share in the market.To maintain advantages in the fierce market competition,enterprises need to adopt more scientific and effective human resource management methods to enhance organizational efficiency and competitiveness.At the same time,this paper analyzes the dilemma faced by enterprise human resource management,points out the new characteristics of enterprise human resource management enabled by big data,and puts forward feasible suggestions for enterprise digital transformation.

Research on Tax Risk Regulation and Strategic Management in the Context of Big Data

With the rapid development of big data,big data has been more and more applied in all walks of life.Under the big data environment,massive big data provides convenience for regional tax risk control and strategic decision-making but also increases the difficulty of data supervision and management.By analyzing the status quo of big data and tax risk management,this paper finds many problems and puts forward effective countermeasures for tax risk supervision and strategic management by using big data.

Exploration and Practice of Big Data Introductory Courses for Big Data Management and Application Majors

As an introductory course for the emerging major of big data management and application,“Introduction to Big Data”has not yet formed a curriculum standard and implementation plan that is widely accepted and used by everyone.To this end,we discuss some of our explorations and attempts in the construction and teaching process of big data courses for the major of big data management and application from the perspective of course planning,course implementation,and course summary.After interviews with students and feedback from questionnaires,students are highly satisfied with some of the teaching measures and programs currently adopted.

Research on Enterprise Human Resource Management Reform Strategy in the Era of Big Data

In the 21st century,with the development of the Internet,mobile devices,and information technology,society has entered a new era:the era of big data.With the help of big data technology,enterprises can obtain massive market and consumer data,realize in-depth analysis of business and market,and enable enterprises to have a deeper understanding of consumer needs,preferences,and behaviors.At the same time,big data technology can also help enterprises carry out human resource management innovation and improve the performance and competitiveness of enterprises.Of course,from another perspective,enterprises in this era are also facing severe challenges.In the face of massive data processing and analysis,it requires superb data processing and analysis capabilities.Secondly,enterprises need to reconstruct their management system to adapt to the changes in the era of big data.Enterprises must treat data as assets and establish a perfect data management system.In addition,enterprises also need to pay attention to protecting customer privacy and data security to avoid data leakage and abuse.In this context,this paper will explore the thinking of enterprise human resource management innovation in the era of big data,and put forward some suggestions on enterprise human resource management innovation.

Data Mining-Based Maintenance Management Framework of Multi-component System

Complex repairable system is composed of thousands of components.Some maintenance management and decision problems in maintenance management and decision need to classify a set of components into several classes based on data mining.Furthermore,with the complexity of industrial equipment increasing,the managers should pay more attention to the key components and carry out the lean management is very important.Therefore,the idea"customer segmentation"of"precise marketing"can be used in the maintenance management of the multi-component system.Following the idea of segmentation,the components of multicomponent systems should be subdivied into groups based on specific attributes relevant to maintenance,such as maintenance cost,mean time between failures,and failure frequency.For the target specific groups of parts,the optimal maintenance policy,health assessment and maintenance scheduling can be determined.The proposed analysis framework will be given out.In order to illustrate the effectiveness of this method,a numerical example is given out.

Privacy Data Management Mechanism Based on Blockchain and Federated Learning

Due to the extensive use of various intelligent terminals and the popularity of network social tools,a large amount of data in the field of medical emerged.How to manage these massive data safely and reliably has become an important challenge for the medical network community.This paper proposes a data management framework of medical network community based on Consortium Blockchain(CB)and Federated learning(FL),which realizes the data security sharing between medical institutions and research institutions.Under this framework,the data security sharing mechanism of medical network community based on smart contract and the data privacy protection mechanism based on FL and alliance chain are designed to ensure the security of data and the privacy of important data in medical network community,respectively.An intelligent contract system based on Keyed-Homomorphic Public Key(KH-PKE)Encryption scheme is designed,so that medical data can be saved in the CB in the form of ciphertext,and the automatic sharing of data is realized.Zero knowledge mechanism is used to ensure the correctness of shared data.Moreover,the zero-knowledge mechanism introduces the dynamic group signature mechanism of chosen ciphertext attack(CCA)anonymity,which makes the scheme more efficient in computing and communication cost.In the end of this paper,the performance of the scheme is analyzed fromboth asymptotic and practical aspects.Through experimental comparative analysis,the scheme proposed in this paper is more effective and feasible.

Optimal Machine Learning Enabled Performance Monitoring for Learning Management Systems

Learning Management System(LMS)is an application software that is used in automation,delivery,administration,tracking,and reporting of courses and programs in educational sector.The LMS which exploits machine learning(ML)has the ability of accessing user data and exploit it for improving the learning experience.The recently developed artificial intelligence(AI)and ML models helps to accomplish effective performance monitoring for LMS.Among the different processes involved in ML based LMS,feature selection and classification processesfind beneficial.In this motivation,this study introduces Glowworm-based Feature Selection with Machine Learning Enabled Performance Monitoring(GSO-MFWELM)technique for LMS.The key objective of the proposed GSO-MFWELM technique is to effectually monitor the performance in LMS.The pro-posed GSO-MFWELM technique involves GSO-based feature selection techni-que to select the optimal features.Besides,Weighted Extreme Learning Machine(WELM)model is applied for classification process whereas the parameters involved in WELM model are optimallyfine-tuned with the help of May-fly Optimization(MFO)algorithm.The design of GSO and MFO techniques result in reduced computation complexity and improved classification performance.The presented GSO-MFWELM technique was validated for its performance against benchmark dataset and the results were inspected under several aspects.The simulation results established the supremacy of GSO-MFWELM technique over recent approaches with the maximum classification accuracy of 0.9589.

Automating the Competence Matrix of a Quality Control Laboratory with the Relational Database Management Framework—A Case Study with the Analytical Chemistry Unit of a National Regulatory Agency

This study analyzed the concept of time efficiency in the data management process associated with the personnel training and competence assessments in one of the quality control (QC) laboratories of Nigeria’s Foods and Drugs Authority (NAFDAC). The laboratory administrators were burdened with a lot of mental and paper-based record keeping because the personnel training’s data were managed manually, hence not efficiently processed. The Excel spreadsheet provided by a Purdue doctoral dissertation as a remedial to this challenge was found to be deficient in handling operations in database tables, and therefore did not appropriately address the inefficiencies. Purpose: This study aimed to reduce the time it essentially takes to generate, obtain, manipulate, exchange, and securely store data that are associated with personnel competence training and assessments. Method: The study developed a software system that was integrated with a relational database management system (RDBMS) to improve manual/Excel-based data management procedures. To validate the efficiency of the software the mean operational times in using the Excel-based format were compared with that of the “New” software system. The data were obtained by performing four predefined core tasks for five hypothetical subjects using Excel and the “New” system (the model system) respectively. Results: It was verified that the average time to accomplish the specified tasks using the “New” system (37.08 seconds) was significantly (p = 0.00191, α = 0.05) lower than the time measurements for the Excel system (77.39 seconds) in the ANACHEM laboratory. The RDBMS-based “New” system provided operational (time) efficiency in the personnel training and competence assessment process in the QC laboratory and reduced human errors.

A Trailblazing Framework of Security Assessment for Traffic Data Management

Connected and autonomous vehicles are seeing their dawn at this moment.They provide numerous benefits to vehicle owners,manufacturers,vehicle service providers,insurance companies,etc.These vehicles generate a large amount of data,which makes privacy and security a major challenge to their success.The complicated machine-led mechanics of connected and autonomous vehicles increase the risks of privacy invasion and cyber security violations for their users by making them more susceptible to data exploitation and vulnerable to cyber-attacks than any of their predecessors.This could have a negative impact on how well-liked CAVs are with the general public,give them a poor name at this early stage of their development,put obstacles in the way of their adoption and expanded use,and complicate the economic models for their future operations.On the other hand,congestion is still a bottleneck for traffic management and planning.This research paper presents a blockchain-based framework that protects the privacy of vehicle owners and provides data security by storing vehicular data on the blockchain,which will be used further for congestion detection and mitigation.Numerous devices placed along the road are used to communicate with passing cars and collect their data.The collected data will be compiled periodically to find the average travel time of vehicles and traffic density on a particular road segment.Furthermore,this data will be stored in the memory pool,where other devices will also store their data.After a predetermined amount of time,the memory pool will be mined,and data will be uploaded to the blockchain in the form of blocks that will be used to store traffic statistics.The information is then used in two different ways.First,the blockchain’s final block will provide real-time traffic data,triggering an intelligent traffic signal system to reduce congestion.Secondly,the data stored on the blockchain will provide historical,statistical data that can facilitate the analysis of traffic conditions according to past behavior.

Digital twin intelligent system for industrial internet of things-based big data management and analysis in cloud environments

This work surveys and illustrates multiple open challenges in the field of industrial Internet of Things(IoT)-based big data management and analysis in cloud environments.Challenges arising from the fields of machine learning in cloud infrastructures,artificial intelligence techniques for big data analytics in cloud environments,and federated learning cloud systems are elucidated.Additionally,reinforcement learning,which is a novel technique that allows large cloud-based data centers,to allocate more energy-efficient resources is examined.Moreover,we propose an architecture that attempts to combine the features offered by several cloud providers to achieve an energy-efficient industrial IoT-based big data management framework(EEIBDM)established outside of every user in the cloud.IoT data can be integrated with techniques such as reinforcement and federated learning to achieve a digital twin scenario for the virtual representation of industrial IoT-based big data of machines and room tem-peratures.Furthermore,we propose an algorithm for determining the energy consumption of the infrastructure by evaluating the EEIBDM framework.Finally,future directions for the expansion of this research are discussed.

CifNet Network Multi-Well Data Management System

The CifNet network multi-well data management system is developed for 100MB or 1000MB local network environments which are used in Chinese oil industry. The kernel techniques of CifNet system include: 1, establishing a high efficient and low cost network multi-well data management architecture based on the General Logging Curve Theory and the Cif data format; 2, implementing efficient visit and transmission of multi-well data in C/S local network based on TCP/IP protocol; 3,ensuring the safety of multi-well data in store, visit and application based on Unix operating system security. By using CifNet system, the researcher in office or at home can visit curves of any borehole in any working area of any oilfield. The application foreground of CifNet system is also commented.

Investigating the Evolution of Human Resource Management in Enterprises in the Era of Big Data

Human resource(HR)management plays a crucial role in the overall management of enterprises,exerting a significant influence on their growth and development.With China now firmly entrenched in the era of big data,the conventional HR management approach is no longer adequate to meet the evolving demands of enterprise progress.Therefore,there is a pressing need to actively revamp the management strategies to improve the quality.This article outlines the importance of reforming enterprise HR management in the context of big data,scrutinizes the prevailing challenges in this domain,explores strategies for transforming HR management within enterprises in the era of big data,and provides illustrative examples to summarize valuable managerial insights,thereby offer enterprise leaders a valuable source of reference information.

Research on the Teaching Reform of Higher Vocational Financial Management Course with the Application of Big Data

With continuous development of modern big data technology,higher vocational financial management teachers should actively seek ways and means of reform.Teaching reform of higher vocational financial management course can be done by integrating modern teaching,understanding the students’academic performance,and comprehensively transforming the teaching methods.These methods can optimize and ensure the comprehensive quality of students,and improve the quality of higher vocational financial management course.

STUDY ON THE BROKEN-ORE DRAWING DATA MANAGEMENT SYSTEM OF SUBLEVEL CAVING

In order to realize the modernized miningmanagement,the authors,based on the practice of a specifiedmine,developed the application software of the broken-oredrawing data management system for sublevel caving throughthe study on FOXBASE computer language.This paper elabo-rates the overall conception of this system,indicates the maintask which should be completed in this system and introduces itsmodule structure and main functions.

System View of Product Data Management and Its Application

A product data management system for a manufacturing enterprise is to make sure that the proper product data can be communicated to the right people at the right time.This paper describes a system analysis paradigm for data analysis in a product data management(PDM)development.Three aspects of the paradigm,i.e.,function,structure and behavior are rep- resented.The use of the paradigm explains why so many kinds of objects are necessary in a commercial database matrix and what models are available for developing a PDM application.As another result,a lot of models are derived from the analysis of product data system paradigm to model product data and PDM database definitions.

Web-Based Data Management and Sharing System for Electron Proke Micro-analysis

This paper proposes a useful web-based system for the management and sharing of electron probe micro-analysis( EPMA)data in geology. A new web-based architecture that integrates the management and sharing functions is developed and implemented.Earth scientists can utilize this system to not only manage their data,but also easily communicate and share it with other researchers.Data query methods provide the core functionality of the proposed management and sharing modules. The modules in this system have been developed using cloud GIS technologies,which help achieve real-time spatial area retrieval on a map. The system has been tested by approximately 263 users at Jilin University and Beijing SHRIMP Center. A survey was conducted among these users to estimate the usability of the primary functions of the system,and the assessment result is summarized and presented.

Study on Material ManagementSystem of Coal Enterprise Basedon Data Warehouse

On the bas is of the reality of material supply management of the coal enterprise, this paper expounds plans of material management systems based on specific IT, and indicates the deficiencies, the problems of them and the necessity of improving them. The structure, models and data organizing schema of the material management decision support system are investigated based on a new data management technology (data warehousing technology).

Developing a Geological Management Information System: National Important Mining Zone Database

Geo-data is a foundation for the prediction and assessment of ore resources, so managing and making full use of those data, including geography database, geology database, mineral deposits database, aeromagnetics database, gravity database, geochemistry database and remote sensing database, is very significant. We developed national important mining zone database (NIMZDB) to manage 14 national important mining zone databases to support a new round prediction of ore deposit. We found that attention should be paid to the following issues: ① data accuracy: integrity, logic consistency, attribute, spatial and time accuracy; ② management of both attribute and spatial data in the same system; ③ transforming data between MapGIS and ArcGIS; ④ data sharing and security; ⑤ data searches that can query both attribute and spatial data. Accuracy of input data is guaranteed and the search, analysis and translation of data between MapGIS and ArcGIS has been made convenient via the development of a checking data module and a managing data module based on MapGIS and ArcGIS. Using ArcSDE, we based data sharing on a client/server system, and attribute and spatial data are also managed in the same system.

Web-based: A data warehouse on osteoporosis data warehouse in the osteoporosis community health information management system

Objective: To establish an interactive management model for community-oriented high-risk osteoporosis in conjunction with a rural community health service center. Materials and Methods: Toward multidimensional analysis of data, the system we developed combines basic principles of data warehouse technology oriented to the needs of community health services. This paper introduces the steps we took in constructing the data warehouse;the case presented here is that of a district community health management information system in Changshu, Jiangsu Province, China. For our data warehouse, we chose the MySQL 4.5 relational database, the Browser/Server, (B/S) model, and hypertext preprocessor as the development tools. Results: The system allowed online analysis processing and next-stage work preparation, and provided a platform for data management, data query, online analysis, etc., in community health service center, specialist outpatient for osteoporosis, and health administration sectors. Conclusion: The users of remote management system and data warehouse can include community health service centers, osteoporosis departments of hospitals, and health administration departments;provide reference for policymaking of health administrators, residents’ health information, and intervention suggestions for general practitioners in community health service centers, patients’ follow-up information for osteoporosis specialists in general hospitals.

A Rule Management System for Knowledge Based Data Cleaning

In this paper, we propose a rule management system for data cleaning that is based on knowledge. This system combines features of both rule based systems and rule based data cleaning frameworks. The important advantages of our system are threefold. First, it aims at proposing a strong and unified rule form based on first order structure that permits the representation and management of all the types of rules and their quality via some characteristics. Second, it leads to increase the quality of rules which conditions the quality of data cleaning. Third, it uses an appropriate knowledge acquisition process, which is the weakest task in the current rule and knowledge based systems. As several research works have shown that data cleaning is rather driven by domain knowledge than by data, we have identified and analyzed the properties that distinguish knowledge and rules from data for better determining the most components of the proposed system. In order to illustrate our system, we also present a first experiment with a case study at health sector where we demonstrate how the system is useful for the improvement of data quality. The autonomy, extensibility and platform-independency of the proposed rule management system facilitate its incorporation in any system that is interested in data quality management.