{"title":"在Hadoop平台上使用R语言进行Twitter数据分析和可视化","authors":"M. Sarnovský, P. Butka, Andrea Huzvarova","doi":"10.1109/SAMI.2017.7880327","DOIUrl":null,"url":null,"abstract":"The main objective of the work presented within this paper was to design and implement the system for twitter data analysis and visualization in R environment using the big data processing technologies. Our focus was to leverage existing big data processing frameworks with its storage and computational capabilities to support the analytical functions implemented in R language. We decided to build the backend on top of the Apache Hadoop framework including the Hadoop HDFS as a distributed filesystem and MapReduce as a distributed computation paradigm. RHadoop packages were then used to connect the R environment to the processing layer and to design and implement the analytical functions in a distributed manner. Visualizations were implemented on top of the solution as a RShiny application.","PeriodicalId":105599,"journal":{"name":"2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Twitter data analysis and visualizations using the R language on top of the Hadoop platform\",\"authors\":\"M. Sarnovský, P. Butka, Andrea Huzvarova\",\"doi\":\"10.1109/SAMI.2017.7880327\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The main objective of the work presented within this paper was to design and implement the system for twitter data analysis and visualization in R environment using the big data processing technologies. Our focus was to leverage existing big data processing frameworks with its storage and computational capabilities to support the analytical functions implemented in R language. We decided to build the backend on top of the Apache Hadoop framework including the Hadoop HDFS as a distributed filesystem and MapReduce as a distributed computation paradigm. RHadoop packages were then used to connect the R environment to the processing layer and to design and implement the analytical functions in a distributed manner. Visualizations were implemented on top of the solution as a RShiny application.\",\"PeriodicalId\":105599,\"journal\":{\"name\":\"2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI)\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SAMI.2017.7880327\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SAMI.2017.7880327","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Twitter data analysis and visualizations using the R language on top of the Hadoop platform
The main objective of the work presented within this paper was to design and implement the system for twitter data analysis and visualization in R environment using the big data processing technologies. Our focus was to leverage existing big data processing frameworks with its storage and computational capabilities to support the analytical functions implemented in R language. We decided to build the backend on top of the Apache Hadoop framework including the Hadoop HDFS as a distributed filesystem and MapReduce as a distributed computation paradigm. RHadoop packages were then used to connect the R environment to the processing layer and to design and implement the analytical functions in a distributed manner. Visualizations were implemented on top of the solution as a RShiny application.
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