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.

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.

Spatial Pattern of Compulsory Education Resources in Shijingshan District of Beijing under the Background of Big Data

With the continuous development of promoting high-quality and balanced basic education and the continuous advancement of research on spatial layout of school,it aims to ensure that the public enjoys educational resources fairly.Based on 63 items of POI data of primary and secondary schools in Shijingshan District,Beijing,this study analyzes the spatial layout,degree supply,and coverage of primary and secondary schools in Shijingshan District using three spatial analysis methods:ArcGIS average nearest neighbor,kernel density analysis,and standard deviation ellipse.The results show that the spatial layout of primary and secondary schools in Shijingshan District,Beijing is characterized by overall clustering:the central region is high-density distribution,while the eastern and western parts are scattered.Moreover,this spatial structure continues to develop along the northwest-southeast direction.Although the high-density population areas in the nine streets correspond to the high-density distribution of schools,the service scope of primary and secondary schools does not cover all residential areas,and the distribution of residential areas within the coverage range is uneven.At the same time,the actual degree supply in Shijingshan District is insufficient.

Research and Application Progress of Agricultural Big Data Technology and Resources

With the development of Internet of things, cloud computing, mobile Inter- net, the scale of the data shows an alarming growth trend. Agricultural information is an important part of modern agricultural construction, and the development of a- gricultural industry is becoming more and more deeply with the application of infor- mation technology. This paper reviewed the concept and characteristic of big data, development history of big data at home and abroad, and emphatically expounded the connotation of agricultural big data, development status of agricultural big data at home and abroad, as well as the applications of agricultural big data technology, agriculture big data resources and agricultural big data in various fields.

A wave energy resource assessment in the China's seas based on multi-satellite merged radar altimeter data

Wave energy resources are abundant in both offshore and nearshore areas of the China＇s seas. A reliable assessment of the wave energy resources must be performed before they can be exploited. First, for a water depth in offshore waters of China, a parameterized wave power density model that considers the effects of the water depth is introduced to improve the calculating accuracy of the wave power density. Second, wave heights and wind speeds on the surface of the China＇s seas are retrieved from an AVISO multi-satellite altim-eter data set for the period from 2009 to 2013. Three mean wave period inversion models are developed and used to calculate the wave energy period. Third, a practical application value for developing the wave energy is analyzed based on buoy data. Finally, the wave power density is then calculated using the wave field data. Using the distribution of wave power density, the energy level frequency, the time variability indexes, the to-tal wave energy and the distribution of total wave energy density according to a wave state, the offshore wave energy in the China＇s seas is assessed. The results show that the areas of abundant and stable wave energy are primarily located in the north-central part of the South China Sea, the Luzon Strait, southeast of Taiwan in the China＇s seas; the wave power density values in these areas are approximately 14.0–18.5 kW/m. The wave energy in the China’s seas presents obvious seasonal variations and optimal seasons for a wave energy utilization are in winter and autumn. Except for very coastal waters, in other sea areas in the China＇s seas, the energy is primarily from the wave state with 0.5 m≤Hs≤4 m, 4 s≤Te≤10 s whereHs is a significant wave height andTe is an energy period; within this wave state, the wave energy accounts for 80% above of the total wave energy. This characteristic is advantageous to designing wave energy convertors （WECs）. The practical application value of the wave energy is higher which can be as an effective supplement for an energy con-sumption in some areas. The above results are consistent with the wave model which indicates fully that this new microwave remote sensing method altimeter is effective and feasible for the wave energy assessment.

High Capacity Data Hiding in Encrypted Image Based on Compressive Sensing for Nonequivalent Resources

To fulfill the requirements of data security in environments with nonequivalent resources,a high capacity data hiding scheme in encrypted image based on compressive sensing(CS)is proposed by fully utilizing the adaptability of CS to nonequivalent resources.The original image is divided into two parts:one part is encrypted with traditional stream cipher;the other part is turned to the prediction error and then encrypted based on CS to vacate room simultaneously.The collected non-image data is firstly encrypted with simple stream cipher.For data security management,the encrypted non-image data is then embedded into the encrypted image,and the scrambling operation is used to further improve security.Finally,the original image and non-image data can be separably recovered and extracted according to the request from the valid users with different access rights.Experimental results demonstrate that the proposed scheme outperforms other data hiding methods based on CS,and is more suitable for nonequivalent resources.

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.

Dominant woody plant species recognition with a hierarchical model based on multimodal geospatial data for subtropical forests

Since the launch of the Google Earth Engine(GEE)cloud platform in 2010,it has been widely used,leading to a wealth of valuable information.However,the potential of GEE for forest resource management has not been fully exploited.To extract dominant woody plant species,GEE combined Sen-tinel-1(S1)and Sentinel-2(S2)data with the addition of the National Forest Resources Inventory(NFRI)and topographic data,resulting in a 10 m resolution multimodal geospatial dataset for subtropical forests in southeast China.Spectral and texture features,red-edge bands,and vegetation indices of S1 and S2 data were computed.A hierarchical model obtained information on forest distribution and area and the dominant woody plant species.The results suggest that combining data sources from the S1 winter and S2 yearly ranges enhances accuracy in forest distribution and area extraction compared to using either data source independently.Similarly,for dominant woody species recognition,using S1 winter and S2 data across all four seasons was accurate.Including terrain factors and removing spatial correlation from NFRI sample points further improved the recognition accuracy.The optimal forest extraction achieved an overall accuracy(OA)of 97.4%and a maplevel image classification efficacy(MICE)of 96.7%.OA and MICE were 83.6%and 80.7%for dominant species extraction,respectively.The high accuracy and efficacy values indicate that the hierarchical recognition model based on multimodal remote sensing data performed extremely well for extracting information about dominant woody plant species.Visualizing the results using the GEE application allows for an intuitive display of forest and species distribution,offering significant convenience for forest resource monitoring.

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.

How to Apply Data Mining Technology to the Study of Agricultural Information Data Resources?

This paper makes a brief description of the definition and methods of data mining.It describes the characteristics of agricultural data(value delivery,specialization,spatio-temporal bidimensionality)and the status of application of data mining technology in agriculture.

Towards a Spatial Data Infrastructure and an Integrated Management of Groundwater Resources

International studies of expertise have shown that the difficulty of data access is one of the major hindrances that brakes any effort to conduct studies on groundwater. This research paper is an attempt to address this problematic through developing a technical framework to implement a Spatial Data Infrastructure (SDI) with a view to improving the status of access to data related to groundwater in Morocco, and achieving their interoperability. This prototype is primarily based on international standards (OGC & ISO) such as Web Map Service (WMS), Web Feature Service (WFS), Catalog Web Service (CSW) and Sensor Observation Service (SOS) accompanied by the use of associated specifications such as Geography Markup Language (GML) and Filter Encoding (FE). This platform is considered both as a tool for sharing updated data collected from numerous and divers source providers, and as a tool of web-based GIS for groundwater management, which constitutes the basis for decision making.

Research on the Intelligent English Multimedia Teaching Resources Based on the Data Mining

With the emergence of the"Intemet+",all walks of life have undergone tremendous changes.The"Internet+Education"has become a trend of development,bringing new opportunities and challenges for the English teaching and learning.Through the online English learning platform,we can carry out the teaching and learning,analyze the students'learning data and establish the relevant models using the data mining technology,and explore the correlation between the English examinations and various elements,which has important values for the students'learning and the teachers'teaching.The author has made a thorough study of it.Practical teaching has proved that the multimedia teaching system is easier for students to understand,train their thinking abilities,improve their imagination and enrich the amount of the teaching information.This paper constructs a network multimedia teaching system,which enriches the teaching means.

Research on the Development of Ideological and Political Education Resources for College Students Based on the Data Mining Technology

With the rapid development of the computer technologies, the rapid spread and development of the network resources have a significant impact on people's thinking and lifestyle. The ideological and political education in colleges and universities should keep pace with the times, and examine the new ways and resources of the ideological and political education in colleges and universities from the perspective of the times. The high-tech has been applied to the development of the modem education, expanding the teaching space-time, improving the teaching methods of the ideological and political course, and enriching the teaching contents of the ideological and political theoretical courses. The development mechanism of the ideological and political education resources for college students based on the data mining technology is not only in line with the trend of the times, but can also play an ideal role in optimizing the ideological and political education mechanism for college students.

Dynamics and data fitting of a time-delayed SIRS hepatitis B model with psychological inhibition factor and limited medical resources

Hepatitis B is an infectious disease worthy of attention.Considering the incubation period,psychological inhibition factor,vaccine,limited medical resources and horizontal transmission,an SIRS model is proposed to describe hepatitis B transmission dynamics.In order to describe the behavior changes caused by people's psychological changes,the non-monotonic incidence rate is adopted in the model.We use the saturated treatment rate to describe the limited medical resources.Mathematical analysis shows the existence conditions of the equilibria,forward or backward bifurcation,Hopf bifurcation and the Bogdanov-Takens bifurcation.During the observation of the case data of hepatitis B in China,it is found that there are mainly three features,periodic outbreaks,aperiodic outbreaks,and periodic outbreaks turns to aperiodic outbreaks.According to the above features,we select three different representative regions,Jiangxi,Zhejiang province and Beijing,and then use our model to fit the actual monthly hepatitis B case data.The basic reproduction numbers that we estimated are 1.7712,1.4805 and 1.4132,respectively.The results of data fitting are consistent with those of theoretical analysis.According to the sensitivity analysis of Ro,we conclude that reducing contact,increasing treatment rate,strengthening vaccination and revaccinating can effectively prevent and control the prevalence of hepatitis B.

Metallic Mineral Resources Assessment and Analysis System Design

This paper presents the aim and the design structure of the metallic mineral resources assessment and analysis system. This system adopts an integrated technique of data warehouse composed of affairs processing layer and analysis application layer. The affairs processing layer includes multiform databases (such as geological database, geophysical database, geochemical database), while the analysis application layer includes data warehouse, online analysis processing and data mining. This paper also presents in detail the data warehouse of the present system and the appropriate spatial analysis methods and models. Finally, this paper presents the prospect of the system.

Study on wave energy resource assessing method based on altimeter data——A case study in Northwest Pacific

Wave energy resource is a very important ocean renewable energy. A reliable assessment of wave energy resources must be performed before they can be exploited. Compared with wave model, altimeter can provide more accurate in situ observations for ocean wave which can be as a novel method for wave energy assessment.The advantage of altimeter data is to provide accurate significant wave height observations for wave. In order to develop characteristic and advantage of altimeter data and apply altimeter data to wave energy assessment, in this study, we established an assessing method for wave energy in local sea area which is dedicated to altimeter data.This method includes three parts including data selection and processing, establishment of evaluation indexes system and criterion of regional division. Then a case study of Northwest Pacific was performed to discuss specific application for this method. The results show that assessing method in this paper can assess reserves and temporal and spatial distribution effectively and provide scientific references for the siting of wave power plants and the design of wave energy convertors.

Multi-Dimensional Aware Scheduling for Co-optimizing Utilization in Data Center

Resource Scheduling is crucial to data centers. However, most previous works focus only on one-dimensional resource models which ignoring the fact that multiple resources simultaneously utilized, including CPU, memory and network bandwidth. As cloud computing allows uncoordinated and heterogeneous users to share a data center, competition for multiple resources has become increasingly severe. Motivated by the differences on integrated utilization obtained from different packing schemes, in this paper we take the scheduling problem as a multi-dimensional combinatorial optimization problem with constraint satisfaction. With NP hardness, we present Multiple attribute decision based Integrated Resource Scheduling (MIRS), and a novel heuristic algorithm to gain the approximate optimal solution. Refers to simulation results, in face of various workload sets, our algorithm has significant superiorities in terms of efficiency and performance compared with previous methods.

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.