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 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.

Research on the Application of Big Data in Logistics Management

With the high-speed development of China’s information technology,human society has ushered in the age of big data,imposing challenges to many logistics companies.With the continuous changes in the development environment,it requires relevant logistics companies to make reasonable self-adjustment to ensure their adaptability to the development environment.At present,many Chinese logistics companies with relatively lagged management model fail to effectively employ big data analysis technique,and have some shortcomings in talent team construction.Therefore,relevant logistics companies need to reasonably use big data technology,improve the quality of logistics management work,and effectively screen valuable part from mass information,thereby promoting rapid development of China’s logistics industry.In this paper,the application of big data in logistics management is analyzed.Firstly,the concepts and characteristics of big data are introduced and then this paper discusses the necessity of application of big data in logistics management,and proposes specific application countermeasures,in hope of providing some reference to relevant staff.

A Systematic Literature Review on Task Allocation and Performance Management Techniques in Cloud Data Center

As cloud computing usage grows,cloud data centers play an increasingly important role.To maximize resource utilization,ensure service quality,and enhance system performance,it is crucial to allocate tasks and manage performance effectively.The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers.The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies,categories,and gaps.A literature review was conducted,which included the analysis of 463 task allocations and 480 performance management papers.The review revealed three task allocation research topics and seven performance management methods.Task allocation research areas are resource allocation,load-Balancing,and scheduling.Performance management includes monitoring and control,power and energy management,resource utilization optimization,quality of service management,fault management,virtual machine management,and network management.The study proposes new techniques to enhance cloud computing work allocation and performance management.Short-comings in each approach can guide future research.The research’s findings on cloud data center task allocation and performance management can assist academics,practitioners,and cloud service providers in optimizing their systems for dependability,cost-effectiveness,and scalability.Innovative methodologies can steer future research to fill gaps in the literature.

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.

Integration and Application of PDM system and logistics management System

In order to improve the efficiency of product design and reduce the logistics cost, this paper first analyzes the information integration demand of product design department and logistics service department. Based on Web service technology, this paper builds the product logistics service information model for integration. Furthermore, through a sanitary appliance company application, a solid base is laid for increasing the product R＆D speed and logistics service.

AMAD:Adaptive Mapping Approach for Datacenter Networks,an Energy-Friend Resource Allocation Framework via Repeated Leader Follower Game

Cloud Datacenter Network(CDN)providers usually have the option to scale their network structures to allow for far more resource capacities,though such scaling options may come with exponential costs that contradict their utility objectives.Yet,besides the cost of the physical assets and network resources,such scaling may also imposemore loads on the electricity power grids to feed the added nodes with the required energy to run and cool,which comes with extra costs too.Thus,those CDNproviders who utilize their resources better can certainly afford their services at lower price-units when compared to others who simply choose the scaling solutions.Resource utilization is a quite challenging process;indeed,clients of CDNs usually tend to exaggerate their true resource requirements when they lease their resources.Service providers are committed to their clients with Service Level Agreements(SLAs).Therefore,any amendment to the resource allocations needs to be approved by the clients first.In this work,we propose deploying a Stackelberg leadership framework to formulate a negotiation game between the cloud service providers and their client tenants.Through this,the providers seek to retrieve those leased unused resources from their clients.Cooperation is not expected from the clients,and they may ask high price units to return their extra resources to the provider’s premises.Hence,to motivate cooperation in such a non-cooperative game,as an extension to theVickery auctions,we developed an incentive-compatible pricingmodel for the returned resources.Moreover,we also proposed building a behavior belief function that shapes the way of negotiation and compensation for each client.Compared to other benchmark models,the assessment results showthat our proposed models provide for timely negotiation schemes,allowing for better resource utilization rates,higher utilities,and grid-friend CDNs.

IoT-Enabled RFID in Supply Chain Management: A Comprehensive Survey and Future Directions

Radio frequency identification (RFID) has emerged as a pivotal technology in supply chain management (SCM), significantly enhancing its efficiency and effectiveness. When integrated with the internet of things (IoT) to form RFID-IoT, this technology brings transformative advancements to SCM, enabling automated sensing, pervasive computing, and ubiquitous data access across the entire supply chain, from manufacturers and distributors to retailers and consumers. This integration facilitates real-time identification and monitoring of products, enhances various processes, improves logistic tracking, and ensures better product quality management. Despite its promising benefits, the adoption of RFID-IoT in SCM faces several challenges, including technical complexities, data security concerns, and high implementation costs. However, the future potential of RFID-IoT technology remains substantial. It is anticipated that further integration with other emerging technologies, such as block chain and artificial intelligence, will lead to more comprehensive and robust SCM solutions, offering unprecedented levels of transparency, efficiency, and automation in supply chain operations.

Research on Human Resource Management in the ＂Internet plus＂ Era

Today＇s society has entered the Intemet era, the Internet technology has brought great changes to all walks of life.With the development of＂Internet plus＂,traditional industry, service industry and business have to make the appropriate changes to meet the demand of the ＂Internet plus＂ era.Similarly,human resource management has to face new challenges in ＂Intemet plus＂ era.This paper expounds the characteristics of＂Internet plus＂ era and the changes brought to the human resource management,put forward the idea that human resources management should make changes to meet the demand of the ＂Internet plus＂ era from three aspects：big data,decentration and staff self management,establishing a new talent incentive mechanism.

Spatial Data Mining to Support Environmental Management and Decision Making--A Case Study in Brazil

The growth of geo-technologies and the development of methods for spatial data collection have resulted in large spatial data repositories that require techniques for spatial information extraction, in order to transform raw data into useful previously unknown information. However, due to the high complexity of spatial data mining, the need for spatial relationship comprehension and its characteristics, efforts have been directed towards improving algorithms in order to provide an increase of performance and quality of results. Likewise, several issues have been addressed to spatial data mining, including environmental management, which is the focus of this paper. The main original contribution of this work is the demonstration of spatial data mining using a novel algorithm with a multi-relational approach that was applied to a database related to water resource from a certain region of S^o Paulo State, Brazil, and the discussion about obtained results. Some characteristics involving the location of water resources and the profile of who is administering the water exploration were discovered and discussed.

A Latency-Aware and Fault-Tolerant Framework for Resource Scheduling and Data Management in Fog-Enabled Smart City Transportation Systems

Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and smart transportation systems.Fog computing tackles a range of challenges,including processing,storage,bandwidth,latency,and reliability,by locally distributing secure information through end nodes.Consisting of endpoints,fog nodes,and back-end cloud infrastructure,it provides advanced capabilities beyond traditional cloud computing.In smart environments,particularly within smart city transportation systems,the abundance of devices and nodes poses significant challenges related to power consumption and system reliability.To address the challenges of latency,energy consumption,and fault tolerance in these environments,this paper proposes a latency-aware,faulttolerant framework for resource scheduling and data management,referred to as the FORD framework,for smart cities in fog environments.This framework is designed to meet the demands of time-sensitive applications,such as those in smart transportation systems.The FORD framework incorporates latency-aware resource scheduling to optimize task execution in smart city environments,leveraging resources from both fog and cloud environments.Through simulation-based executions,tasks are allocated to the nearest available nodes with minimum latency.In the event of execution failure,a fault-tolerantmechanism is employed to ensure the successful completion of tasks.Upon successful execution,data is efficiently stored in the cloud data center,ensuring data integrity and reliability within the smart city ecosystem.

On Computing the Suitability of Non-Human Resources for Business Process Analysis

Business process improvement is a systematic approach used by several organizations to continuously improve their quality of service.Integral to that is analyzing the current performance of each task of the process and assigning the most appropriate resources to each task.In continuation of our previous work,we categorize resources into human and non-human resources.For instance,in the healthcare domain,human resources include doctors,nurses,and other associated staff responsible for the execution of healthcare activities;whereas the non-human resources include surgical and other equipment needed for execution.In this study,we contend that the two types of resources(human and non-human)have a different impact on the process performance,so their suitability should be measured differently.However,no work has been done to evaluate the suitability of non-human resources for the tasks of a process.Consequently,it becomes difficult to identify and subsequently overcome the inefficiencies caused by the non-human resources to the task.To address this problem,we present a three-step method to compute a suitability score of non-human resources for the task.As an evaluation of the proposed method,a healthcare case study is used to illustrate the applicability of the proposed method.Furthermore,we performed a controlled experiment to evaluate the usability of the proposed method.The encouraging response shows the usefulness of the proposed method.

广义logistic模型的优化开发

在开放式资源的条件下，研究了广义logisti模型的优化开发问题，然后，在受管理资源的条件下，运用庞特里雅金的最大值原理，得到了广义logistic模型的最优开发策略。作为应用，给出了一种特殊形式的最优轨线。

基于logistic模型的企业和谐劳动关系影响因素研究

选取广东省佛山市顺德区国家级和谐劳动关系综合试验区162家企业为样本,以劳动合同、薪酬福利与社会保障、劳动安全卫生保护、员工发展与民主管理、劳动争议及调处等作为劳动关系评价的5个维度.从企业、高管及员工层面基于logistic回归模型研究和谐劳动关系的影响因素.研究结果表明:企业层面,企业的所有权登记状态以及生产方式属性对劳动关系有显著影响.高管层面,年龄、教育程度、是否本企业的业主之一、是否企业的开创者、本行业工作的年限、现企业工作的年限、担任高管职位的年限对劳动关系有显著影响.员工层面,年龄、文化程度、婚姻状况对劳动关系有显著影响.据此,提出了改善劳动关系的具体建议,这对正确认识和处理新时代劳动关系的和谐运行具有一定现实意义.

广义Logistic模型的优化开发

本文首先在开放式资源的条件下,研究了广义Logistic模型的优化开发问题。然后,在受管理资源的条件下,运用庞特里雅金的最大值原理,研究了广义Logistic模型的最优开发策略,作为一个应用,作者给出了一种特殊形式的最优轨线。最后,作者进一步地指明了一些研究方面。

基于Panel Data的信息化与物流产业关系研究

基于Panel Data模型,采用中国31个省份,2005-2015年的面板数据,对信息化与物流产出的关系从静态和动态两个角度分别进行研究.面板数据模型的研究结果表明:信息化对物流产出的提高意义重大,其弹性系数为0.198.再利用向量自回归模型(PVAR)的脉冲响应函数和方差分解动态分析了不同变量冲击下物流产出的变化,结果发现信息化对物流产出的影响趋势最大且有明显的上升趋势,资金投入对物流产出呈现下降趋势,物流设施总体比较平稳.

Competency Driven Resource Evaluation Method for Business Process Intelligence

Enterprises are continuously aiming at improving the execution of processes to achieve a competitive edge.One of the established ways of improving process performance is to assign the most appropriate resources to each task of the process.However,evaluations of business process improvement approaches have established that a method that can guide decision-makers to identify the most appropriate resources for a task of process improvement in a structured way,is missing.It is because the relationship between resources and tasks is less understood and advancement in business process intelligence is also ignored.To address this problem an integrated resource classification framework is presenting that identifies competence,suitability,and preference as the relationship of task with resources.But,only the competence relationship of human resources with a task is presented in this research as a resource competence model.Furthermore,the competency calculation method is presented as a user guider layer for business process intelligencebased resource competence evaluation.The computed capabilities serve as a basic input for choosing the most appropriate resources for each task of the process.Applicability of method is illustrated through a heathcare case study.

Replication-Based Query Management for Resource Allocation Using Hadoop and MapReduce over Big Data

We live in an age where everything around us is being created.Data generation rates are so scary,creating pressure to implement costly and straightforward data storage and recovery processes.MapReduce model functionality is used for creating a cluster parallel,distributed algorithm,and large datasets.The MapReduce strategy from Hadoop helps develop a community of non-commercial use to offer a new algorithm for resolving such problems for commercial applications as expected from this working algorithm with insights as a result of disproportionate or discriminatory Hadoop cluster results.Expected results are obtained in the work and the exam conducted under this job;many of them are scheduled to set schedules,match matrices’data positions,clustering before determining to click,and accurate mapping and internal reliability to be closed together to avoid running and execution times.Mapper output and proponents have been implemented,and the map has been used to reduce the function.The execution input key/value pair and output key/value pair have been set.This paper focuses on evaluating this technique for the efficient retrieval of large volumes of data.The technique allows for capabilities to inform a massive database of information,from storage and indexing techniques to the distribution of queries,scalability,and performance in heterogeneous environments.The results show that the proposed work reduces the data processing time by 30%.

Knowledge Discovery in Data: A Case Study

It is common in industrial construction projects for data to be collected and discarded without being analyzed to extract useful knowledge. A proposed integrated methodology based on a five-step Knowledge Discovery in Data (KDD) model was developed to address this issue. The framework transfers existing multidimensional historical data from completed projects into useful knowledge for future projects. The model starts by understanding the problem domain, industrial construction projects. The second step is analyzing the problem data and its multiple dimensions. The target dataset is the labour resources data generated while managing industrial construction projects. The next step is developing the data collection model and prototype data ware-house. The data warehouse stores collected data in a ready-for-mining format and produces dynamic On Line Analytical Processing (OLAP) reports and graphs. Data was collected from a large western-Canadian structural steel fabricator to prove the applicability of the developed methodology. The proposed framework was applied to three different case studies to validate the applicability of the developed framework to real projects data.

信息资源管理学科视角下的数据要素研究

[目的/意义]数字经济背景下,信息资源管理学科需要进一步融入到数据要素化市场建设之中,积极建构新的学术增长点。[研究设计/方法]首先分析信息资源管理学科与数据要素的关联逻辑以及学科的角色建构问题,而后介绍信息资源管理学科视域下的若干核心议题,最后对两者融合的未来发展提出思考。[结论/发现]数据要素研究是信息资源管理学科的新领域和新方向,应充分发挥学科特色和优势,有组织地推进数据要素研究与实践。[创新/价值]从信息资源管理学科视域思考数据要素研究,为进一步扩大学科研究视野、拓展学科研究空间提供启示,为数据要素化与数字经济发展中的学科话语建构提供参考。