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big data Analytics Using Graph Signal processing

Computers, Materials & Continua 2023年第1期74卷 489-502页

作者： Farhan Amin Omar M.Barukab Gyu Sang Choi Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea Faculty of Computing and Information Technology-Rabigh King Abdulaziz UniversityJeddahSaudi Arabia

The networks are fundamental to our modern world and they appear throughout science and society.Access to a massive amount of data presents a unique opportunity to the researcher’s community.As networks grow in size the complexity increases and our ability to analyze them using the current state of the art is at severe risk of failing to keep pace.Therefore,this paper initiates a discussion on graph signal processing for large-scale data analysis.We first provide a comprehensive overview of core ideas in Graph signal processing(GSP)and their connection to conventional digital signal processing(DSP).We then summarize recent developments in developing basic GSP tools,including methods for graph filtering or graph learning,graph signal,graph Fourier transform(GFT),spectrum,graph frequency,etc.Graph filtering is a basic task that allows for isolating the contribution of individual frequencies and therefore enables the removal of noise.We then consider a graph filter as a model that helps to extend the application of GSP methods to large datasets.To show the suitability and the effeteness,we first created a noisy graph signal and then applied it to the filter.After several rounds of simulation results.We see that the filtered signal appears to be smoother and is closer to the original noise-free distance-based signal.By using this example application,we thoroughly demonstrated that graph filtration is efficient for big data analytics.

关键词： big data data science big data processing graph signal processing social networks

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High-efficient energy saving processing of big data of communication under mobile cloud computing

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International Journal of Modeling, Simulation, and Scientific Computing 2019年第4期10卷 96-106页

作者： Yazhen Liu Pengfei Fan Jiyang Zhu Liping Wen Xiongfei Fan Information Communication Operation and Maintenance Center Information and Communication Branch State Grid Inner Mongolia Eastern Electric Power Co. Ltd.HohhotInner Mongolia Autonomous Region 010020P.R.China Hohhot Power Supply Bureau Inner Mongolia Power(Group)Co.Ltd.HohhotInner Mongolia Autonomous Region 010020P.R.China Training Center Inner Mongolia Power(Group)Co.Ltd.HohhotInner Mongolia Autonomous Region 010020P.R.China

From 21st century,it is hard for traditional storage and algorithm to provide service with high quality because of big data of communication which grows rapidly.Thus,cloud computing technology with relatively low cost of hardware facilities is created.However,to guarantee the quality of service in the situation of the rapid growth of data volume,the energy consumption cost of cloud computing begins to exceed the hardware cost.In order to solve the problems mentioned above,this study briefly introduced the virtual machine and its energy consumption model in the mobile cloud environment,introduced the basic principle of the virtual machine migration strategy based on the artificial bee colony algorithm and then simulated the performance of processing strategy to big data of communication based on artificial bee colony algorithm in mobile cloud computing environment by CloudSim3.0 software,which was compared with the performance of two algorithms,resource management(RM)and genetic algorithm(GA).The results showed that the power consumption of the migration strategy based on the artificial bee colony algorithm was lower than the other two strategies,and there were fewer failed virtual machines under the same number of requests,which meant that the service quality was higher.

关键词： Mobile cloud computing big data processing artificial bee colony algorithm energy saving

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Metaheuristic Clustering Protocol for Healthcare dataCollection in MobileWireless Multimedia Sensor Networks

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Computers, Materials & Continua 2021年第3期66卷 3215-3231页

作者： G G.Kadiravan P.Sujatha T.Asvany R.Punithavathi Mohamed Elhoseny Irina V.Pustokhina Denis A.Pustokhin K.Shankar Department of Computer Science Pondicherry UniversityPuducherry605014India Department of Information Technology M.Kumarasamy College of EngineeringKarur639113India Faculty of Computers and Information Mansoura UniversityEgypt Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudiIndia

Nowadays,healthcare applications necessitate maximum volume of medical data to be fed to help the physicians,academicians,pathologists,doctors and other healthcare professionals.Advancements in the domain of Wireless Sensor Networks(WSN)andMultimediaWireless Sensor Networks(MWSN)are tremendous.M-WMSN is an advanced form of conventional Wireless Sensor Networks(WSN)to networks that use multimedia devices.When compared with traditional WSN,the quantity of data transmission in M-WMSN is significantly high due to the presence of multimedia content.Hence,clustering techniques are deployed to achieve low amount of energy utilization.The current research work aims at introducing a new Density Based Clustering(DBC)technique to achieve energy efficiency inWMSN.The DBC technique is mainly employed for data collection in healthcare environment which primarily depends on three input parameters namely remaining energy level,distance,and node centrality.In addition,two static data collector points called Super Cluster Head(SCH)are placed,which collects the data from normal CHs and forwards it to the Base Station(BS)directly.SCH supports multi-hop data transmission that assists in effectively balancing the available energy.Adetailed simulation analysiswas conducted to showcase the superior performance of DBC technique and the results were examined under diverse aspects.The simulation outcomes concluded that the proposed DBC technique improved the network lifetime to a maximum of 16,500 rounds,which is significantly higher compared to existing methods.

关键词： Smart sensor environment healthcare data multimedia big data processing clustering mobility energy efficiency

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Insights into CODE-DE–Germany’s Copernicus data and exploitation platform

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big Earth data 2019年第4期3卷 338-361页

作者： Tobias Storch Christoph Reck Stefanie Holzwarth Bert Wiegers Nico Mandery Ulrich Raapea Christian Strobl Rouven Volkmann Martin Böttcher Andreas Hirner Johanna Senft Nicolae Plesia Thomas Kukuk Stephan Meissl Jens-Rainer Felske Thomas Heege Vanessa Keuck Michael Schmidt Helmut Staudenrausch Earth Observation Center(EOC) German Aerospace Center(DLR)WeßlingGermany T-Systems SfR GmbH BonnGermany Brockmann Consult GmbH GeesthachtGermany GAF AG MunichGermany EOX IT Services GmbH ViennaAustria Werum Software&Systems AG LüneburgGermany EOMAP GmbH&Co.KG SeefeldGermany Programme Strategy Space R&D German Aerospace Center(DLR)CologneGermany Space Administration German Aerospace Center(DLR)BonnGermany

This article presents and analyses the modular architecture and capabilities of CODE-DE(Copernicus data and Exploitation Platform–Deutschland,***),the integrated German operational environment for accessing and processing Copernicus data and products,as well as the methodology to establish and operate the system.Since March 2017,CODE-DE has been online with access to Sentinel-1 and Sentinel-2 data,to Sentinel-3 data shortly after this time,and since March 2019 with access to Sentinel-5P data.These products are available and accessed by 1,682 registered users as of March 2019.During this period 654,895 products were downloaded and a global catalogue was continuously updated,featuring a data volume of 814 TByte based on a rolling archive concept supported by a reload mechanism from a long-term archive.Since November 2017,the element for big data processing has been operational,where registered users can process and analyse data themselves specifically assisted by methods for value-added product generation.Utilizing 195,467 core and 696,406 memory hours,982,948 products of different applications were fully automatically generated in the cloud environment and made available as of March 2019.Special features include an improved visualization of available Sentinel-2 products,which are presented within the catalogue client at full 10 m resolution.

关键词： big earth data big data processing online data access Sentinel Copernicus CODE-DE collaborative ground segment

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