{"title":"地理空间大数据可扩展处理的雾计算架构","authors":"Rabindra Kumar Barik, R. Priyadarshini, R. K. Lenka, Harishchandra Dubey, K. Mankodiya","doi":"10.4018/ijagr.2020010101","DOIUrl":null,"url":null,"abstract":"Geospatialdataanalysisusingcloudcomputingplatformisoneofthepromisingareasforanalysing, retrieving,andprocessingvolumetricdata.Fogcomputingparadigmassistscloudplatformwherefog devicestrytoincreasethethroughputandreducelatencyattheedgeoftheclient.Inthisresearchpaper, theauthorsdiscusstwocasestudiesongeospatialdataanalysisusingFog-assistedcloudcomputing namely,(1)GangaRiverBasinManagementSystem;and(2)TourismInformationManagementof India.BothcasestudiesevaluateproposedGeoFogarchitectureforefficientanalysisandmanagement ofgeospatial bigdata employing fog computing.The authorsdevelopedaprototypeofGeoFog architectureusingIntelEdisonandRaspberryPidevices.Theauthors implementedsomeof the opensourcecompressionmethodsforreducingthedatatransmissionoverloadinthecloud.Proposed architectureperformsdatacompressionandoverlayanalysisofdata.Theauthorsfurtherdiscussed theimprovementinscalabilityandtimeanalysisusingproposedGeoFogarchitectureandGeospark tool.Discussedresultsshowthemeritoffogcomputingthatholdsanenormouspromiseforenhanced analysisofgeospatialbigdatainriverGangabasinandtourisminformationmanagementscenario. KeywoRDS Cloud Computing, Geospatial Big Data, Geospatial Data, K-Means, Open Source GIS, Overlay Analysis, River, Tourism, Visualization","PeriodicalId":43062,"journal":{"name":"International Journal of Applied Geospatial Research","volume":null,"pages":null},"PeriodicalIF":0.4000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4018/ijagr.2020010101","citationCount":"6","resultStr":"{\"title\":\"Fog Computing Architecture for Scalable Processing of Geospatial Big Data\",\"authors\":\"Rabindra Kumar Barik, R. Priyadarshini, R. K. Lenka, Harishchandra Dubey, K. Mankodiya\",\"doi\":\"10.4018/ijagr.2020010101\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Geospatialdataanalysisusingcloudcomputingplatformisoneofthepromisingareasforanalysing, retrieving,andprocessingvolumetricdata.Fogcomputingparadigmassistscloudplatformwherefog devicestrytoincreasethethroughputandreducelatencyattheedgeoftheclient.Inthisresearchpaper, theauthorsdiscusstwocasestudiesongeospatialdataanalysisusingFog-assistedcloudcomputing namely,(1)GangaRiverBasinManagementSystem;and(2)TourismInformationManagementof India.BothcasestudiesevaluateproposedGeoFogarchitectureforefficientanalysisandmanagement ofgeospatial bigdata employing fog computing.The authorsdevelopedaprototypeofGeoFog architectureusingIntelEdisonandRaspberryPidevices.Theauthors implementedsomeof the opensourcecompressionmethodsforreducingthedatatransmissionoverloadinthecloud.Proposed architectureperformsdatacompressionandoverlayanalysisofdata.Theauthorsfurtherdiscussed theimprovementinscalabilityandtimeanalysisusingproposedGeoFogarchitectureandGeospark tool.Discussedresultsshowthemeritoffogcomputingthatholdsanenormouspromiseforenhanced analysisofgeospatialbigdatainriverGangabasinandtourisminformationmanagementscenario. KeywoRDS Cloud Computing, Geospatial Big Data, Geospatial Data, K-Means, Open Source GIS, Overlay Analysis, River, Tourism, Visualization\",\"PeriodicalId\":43062,\"journal\":{\"name\":\"International Journal of Applied Geospatial Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.4000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.4018/ijagr.2020010101\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Applied Geospatial Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/ijagr.2020010101\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"GEOGRAPHY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Applied Geospatial Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijagr.2020010101","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"GEOGRAPHY","Score":null,"Total":0}
引用次数: 6
Fog Computing Architecture for Scalable Processing of Geospatial Big Data
Geospatialdataanalysisusingcloudcomputingplatformisoneofthepromisingareasforanalysing, retrieving,andprocessingvolumetricdata.Fogcomputingparadigmassistscloudplatformwherefog devicestrytoincreasethethroughputandreducelatencyattheedgeoftheclient.Inthisresearchpaper, theauthorsdiscusstwocasestudiesongeospatialdataanalysisusingFog-assistedcloudcomputing namely,(1)GangaRiverBasinManagementSystem;and(2)TourismInformationManagementof India.BothcasestudiesevaluateproposedGeoFogarchitectureforefficientanalysisandmanagement ofgeospatial bigdata employing fog computing.The authorsdevelopedaprototypeofGeoFog architectureusingIntelEdisonandRaspberryPidevices.Theauthors implementedsomeof the opensourcecompressionmethodsforreducingthedatatransmissionoverloadinthecloud.Proposed architectureperformsdatacompressionandoverlayanalysisofdata.Theauthorsfurtherdiscussed theimprovementinscalabilityandtimeanalysisusingproposedGeoFogarchitectureandGeospark tool.Discussedresultsshowthemeritoffogcomputingthatholdsanenormouspromiseforenhanced analysisofgeospatialbigdatainriverGangabasinandtourisminformationmanagementscenario. KeywoRDS Cloud Computing, Geospatial Big Data, Geospatial Data, K-Means, Open Source GIS, Overlay Analysis, River, Tourism, Visualization
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