Research On Big Data Graphs Summarization Theory And Applications In The Cloud Environment

In recent years,due to the rapid development of mobile networks and the rapid expansion of access to network devices,the scale of data in the fields of computers and social networks has grown dramatically.Basically,Graph data structure represent the relationship and dependency in the data,therefore,the data in the current cloud environment generally use the graph as a data structure for management and operation.However,the traditional graph analysis tools and algorithms to deal with such large-scale data gradually becomes incompetent.It is challenging and worthwhile for traditional classic tools and techniques to update and improve the processing of such graphs with large-scale data,such as how to reduce the size of such graph data structures and how to extract information from large-scale graph data structures is a challenging problem.To minimize size of the Big Data graph structure,and retain the information of the original data set,so that it can be used by existing technologies such as data mining,information extraction,and recommendation systems for traditional data sizes,is known as graph minimization theory.The research and development of this part has become the focus of attention in academia and industry.In view of the fact that many modern technologies have introduced virtualization ideas to improve processing efficiency,such as virtualized networks,virtualized storage,and other emerging technologies,these technologies have had an important impact in the field of computer science.In order to improve the efficiency of mining large-scale graph data and handle large-scale graph data more effectively,we have proposed a new method called "vGraph" by leveraging virtualization.vGraph creates a new virtual plane above the data plane where the original large-scale data graph is located,and builds a new graph in it.This method uses the calculated values of the cumulative similarity to create virtual nodes and edges to replace the nodes and edges of the original data plane.By conducting experiments on virtual datasets and real-world datasets,we verified that the use of vGraph in the processing of large-scale graph data can effectively reduce the amount of data processing,reduce memory usage,and increase processing speed.Whether it is time or space,using vGraph instead of the current Big Data graph data structure is effective.Applying the theory of minimization of Big Data graph proposed in this research,good practice results have been obtained in service computing scenarios,e-commerce scenarios,and industrial 4.0 scenarios in the cloud environment.1)The development of Service Computing promotes the advent of the XaaS era and also brings significant changes to cloud computing.It offers new opportunities for business modeling,management,and e-commerce.Online shopping is one of the most widely used applications in the field of e-commerce.However,the phenomenon of low penetration of e-commerce in developing countries,especially in rural areas,still exists.Low Internet access,few Internet users,and imperfect credit systems are the significant reasons that hinder the development of e-commerce in developing countries.There is a graph structure with large-scale data between customers and online sellers in e-commerce.Starting from this data graph structure,this research proposes an OSaaS model based on cloud service center.The experimental results show that by using this model,users can increase the use of online shopping applications and expand the scale of transaction data.The model introduces a cloudservice center that plays a third-party role between consumers and online sellers.Consumers can place orders through the phone to a local cloud service center,or they can obtain orders by visiting the service points located in the community.Experimental analysis also shows that this approach can accelerate the development of online shopping applications in developing countries.2)The rapid development of information and communication technology also impacts the traditional trading platform and pushes world of trade into the information age.Due to the rapid development of information and communication technology,the source of e-commerce data in developed countries has changed from a traditional single desktop computer to a diversified intelligent terminal.On the contrary,due to some obstacles,the progress of developing countries in the acceptance of e-commerce,which is manifested as the speed of generating e-commerce Big Data graph has been slow.This is mainly due to the lack of emerging information and communication technologies in developing countries compared to developed countries,high illiteracy rates,unsound social credit protection systems,low acceptance of emerging technologies by citizens,and limited availability of Internet access.This article takes Pakistan as a case study of developing countries to study indirectly the limitations,challenges,and barriers to the development of online shopping applications by studying the generation speed of e-commerce Big Data graphs.On the basis of full theoretical research,a research model was put forward,and empirical analysis showed the main barriers and difficulties faced by Pakistani countries in order to increase the penetration of online shopping applications.3)With the development of Industry 4.0,the world is at the beginning of a new era of innovation in industrial automation technology.The global modern industrial system combines the capabilities of machines,computing,analytics,Internet of Things,cloud systems,and automated data exchange.Industry 4.0 is a revolution in the digital world of digital factories and smart products.Industry 4.0 focuses on emerging technologies such as network physical systems,product line intelligence,human-computer interaction,3D printing,etc.It is an integration of multidisciplinary technologies,and users can get incredible and convenient services through simple interaction.The rapid development of Internet of Things technology has enabled devices to easily add communications capabilities to interact with other devices,connecting the entire world of intelligent machines.At the same time,it also produced inconsistent data sizes.Big Data has brought new opportunities to these systems.This article takes the cloud environment under industry 4.0 as the background,discusses how to apply the large-scale graph to abstract information technology.The research results in this section will help researchers use the cloud data in the Industry 4.0 field as a prototype,and then design Big Data diagrams in different production links to extract abstract information technology.