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.

Optimization Management of Industrial Organizations Based on Performance Indicators

This paper proposes an intelligent management system (IMS) to help managers in their delicate and tedious task of exploiting the plethora of data (indicators) contained in management dashboards. This system is based on intelligent agents, ontologies and data mining. It is implemented by PASSI (Process for Agent Societies Specification and Implementation) methods for agent design and implementation, the Methodology for Knowledge Modeling and Hot-Winters for data prediction. Intelligent agents not only track indicators but also store the knowledge of managers within the company. Ontologies are used to manage the representation and presentation aspects of knowledge. Data mining makes it possible to: make the most of all available data;model the industrial process of data selection, exploration and modeling;and transform behaviors into predictive indicators. An instance of the IMS named SYGISS, currently in operation within a large brewery organization, allows us to observe very interesting results: the extraction of indicators is done in less than 5 minutes whereas manual extraction used to take 14 days;the generation of dashboards is instantaneous whereas it used to take 12 hours;the interpretation of indicators is instantaneous whereas it used to take a day;forecasts are possible and are done in less than 5 minutes whereas they did not exist with the old management. These important contributions help to optimize the management of this organization.

Whole-Process Project Cost Management Based on Building Information Modeling (BIM)

The whole-process project cost management based on building information modeling(BIM)is a new management method,aiming to realize the comprehensive optimization and improvement of project cost management through the application of BIM technology.This paper summarizes and analyzes the whole-process project cost management based on BIM,aiming to explore its application and development prospects in the construction industry.Firstly,this paper introduces the role and advantages of BIM technology in engineering cost management,including information integration,data sharing,and collaborative work.Secondly,the paper analyzes the key technologies and methods of the whole-process project cost management based on BIM,including model construction,data management,and cost control.In addition,the paper also discusses the challenges and limitations of the whole-process BIM project cost management,such as the inconsistency of technical standards,personnel training,and consciousness change.Finally,the paper summarizes the advantages and development prospects of the whole-process project cost management based on BIM and puts forward the direction and suggestions for future research.Through the research of this paper,it can provide a reference for construction cost management and promote innovation and development in the construction industry.

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.

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.

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.

Big data management in the mining industry

The mining industry faces a number of challenges that promote the adoption of new technologies.Big data,which is driven by the accelerating progress of information and communication technology,is one of the promising technologies that can reshape the entire mining landscape.Despite numerous attempts to apply big data in the mining industry,fundamental problems of big data,especially big data management(BDM),in the mining industry persist.This paper aims to fill the gap by presenting the basics of BDM.This work provides a brief introduction to big data and BDM,and it discusses the challenges encountered by the mining industry to indicate the necessity of implementing big data.It also summarizes data sources in the mining industry and presents the potential benefits of big data to the mining industry.This work also envisions a future in which a global database project is established and big data is used together with other technologies(i.e.,automation),supported by government policies and following international standards.This paper also outlines the precautions for the utilization of BDM in the mining industry.

A Method for Trust Management in Cloud Computing: Data Coloring by Cloud Watermarking

With the development of Internet technology and human computing, the computing environment has changed dramatically over the last three decades. Cloud computing emerges as a paradigm of Internet computing in which dynamical, scalable and often virtuMized resources are provided as services. With virtualization technology, cloud computing offers diverse services （such as virtual computing, virtual storage, virtual bandwidth, etc.） for the public by means of multi-tenancy mode. Although users are enjoying the capabilities of super-computing and mass storage supplied by cloud computing, cloud security still remains as a hot spot problem, which is in essence the trust management between data owners and storage service providers. In this paper, we propose a data coloring method based on cloud watermarking to recognize and ensure mutual reputations. The experimental results show that the robustness of reverse cloud generator can guarantee users＇ embedded social reputation identifications. Hence, our work provides a reference solution to the critical problem of cloud security.

Data Mining in Customer Relationship Management in companies

More and more practice proves that focusing on customer needs is the key to business success.So studying on customer relationship management is very important for its implementation in enterprises.In recent years,data mining in customer relationship management(CRM) application has always been one of the hot spots.This paper shows the relevant methods of data mining application in CRM taking Telecom as an example.

An Ontology Reasoning Architecture for Data Mining Knowledge Management

In order to realize the intelligent management of data mining （DM） domain knowledge, this paper presents an architecture for DM knowledge management based on ontology. Using ontology database, this architecture can realize intelligent knowledge retrieval and automatic accomplishment of DM tasks by means of ontology services. Its key features include：①Describing DM ontology and meta-data using ontology based on Web ontology language （OWL）.② Ontology reasoning function. Based on the existing concepts and relations, the hidden knowledge in ontology can be obtained using the reasoning engine. This paper mainly focuses on the construction of DM ontology and the reasoning of DM ontology based on OWL DL（s）.

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.

Innovative Applications of Patient Experienee Big Data in Modern Hospital Management Improve Healthcare Quality

It is important for modern hospital management to strengthen medical humanistic care and build a harmonious doctor-patient relationship.Innovative applications of the big data resources of patient experience in modern hospital management facilitate hospital management to realize real-time supervision,dynamic management and s&entitle decision-making based on patients experiences.It is helping the transformation of hospital management from an administrator^perspective to a patients perspective,and from experience-driven to data-driven.The technological innovations in hospital management based on patient experience data can assist the optimization and continuous improvement of healthcare quality,therefore help to increase patient satisfaction to the medical services.

Blockchain for Transparent Data Management Toward 6G

The wealth of user data acts as a fuel for network intelligence toward the sixth generation wireless networks(6G).Due to data heterogeneity and dynamics,decentralized data management(DM)is desirable for achieving transparent data operations across network domains,and blockchain can be a promising solution.However,the increasing data volume and stringent data privacy-preservation requirements in 6G bring significantly technical challenge to balance transparency,efficiency,and privacy requirements in decentralized blockchain-based DM.In this paper,we investigate blockchain solutions to address the challenge.First,we explore the consensus protocols and scalability mechanisms in blockchains and discuss the roles of DM stakeholders in blockchain architectures.Second,we investigate the authentication and authorization requirements for DM stakeholders.Third,we categorize DM privacy requirements and study blockchain-based mechanisms for collaborative data processing.Subsequently,we present research issues and potential solutions for blockchain-based DM toward 6G from these three perspectives.Finally,we conclude this paper and discuss future research directions.

Integrity-Management Characteristics and Efficiency Evaluation of Oil and Gas Pipelines

Pipeline integrity is a cornerstone of the operation of many industrial systems, and maintaining pipeline integrity is essential for preventing economic losses and ecological damage caused by oil and gas leaks. Based on integritymanagement data published by the US Pipeline and Hazardous Materials Safety Administration, this study applied the k-means clustering and data envelopment analysis(DEA) methods to both explore the characteristics of pipeline-integrity management and evaluate its efficiency. The k-means clustering algorithm was found to be scientifically valid for classifying pipeline companies as either low-, medium-, or high-difficulty companies according to their integrity-management requirements. Regardless of a pipeline company's classification, equipment failure was found to be the main cause of pipeline failure. In-line inspection corrosion and dent tools were the two most-used tools for pipeline inspection. Among the types of repair, 180-day condition repairs were a key concern for pipeline companies. The results of the DEA analysis indicate that only three out of 34 companies were deemed to be DEA-effective. To improve the effectiveness of pipeline integrity management, we propose targeted directions and scales of improvement for non-DEA-effective companies.

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 AreSDE, we based data sharing on a client/server system, and attribute and spatial data are also managed in the same system.