{"title":"基于Karma建模的大数据集成研究","authors":"Wang Xiao, Liu Guoqi, L. Bin","doi":"10.1109/ICSESS.2017.8342906","DOIUrl":null,"url":null,"abstract":"Aiming at the problem of data integration about heterogeneous and large amount of data in big data 4V features, the method of data integration based on Karma modeling is explored, and the data set of literature area is used as an example to verify the method. First of all, analyze specifically part of the literature data sets that are obtained. And then using Protégé ontology modeling tool to build the related domain ontology. Through the Karma modeling tool, the literature data set is mapped to the literature domain ontology and uniformly published as RDF data so that the semantic mapping is achieved, which effectively solve the important problem of multi-source and heterogeneous data. The Karma model that is built and published will be applied to complete big data set for big data integration. Finally, we sum up the results of the practice and address our future works.","PeriodicalId":179815,"journal":{"name":"2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Research on big data integration based on Karma modeling\",\"authors\":\"Wang Xiao, Liu Guoqi, L. Bin\",\"doi\":\"10.1109/ICSESS.2017.8342906\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aiming at the problem of data integration about heterogeneous and large amount of data in big data 4V features, the method of data integration based on Karma modeling is explored, and the data set of literature area is used as an example to verify the method. First of all, analyze specifically part of the literature data sets that are obtained. And then using Protégé ontology modeling tool to build the related domain ontology. Through the Karma modeling tool, the literature data set is mapped to the literature domain ontology and uniformly published as RDF data so that the semantic mapping is achieved, which effectively solve the important problem of multi-source and heterogeneous data. The Karma model that is built and published will be applied to complete big data set for big data integration. Finally, we sum up the results of the practice and address our future works.\",\"PeriodicalId\":179815,\"journal\":{\"name\":\"2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSESS.2017.8342906\",\"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 8th IEEE International Conference on Software Engineering and Service Science (ICSESS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSESS.2017.8342906","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on big data integration based on Karma modeling
Aiming at the problem of data integration about heterogeneous and large amount of data in big data 4V features, the method of data integration based on Karma modeling is explored, and the data set of literature area is used as an example to verify the method. First of all, analyze specifically part of the literature data sets that are obtained. And then using Protégé ontology modeling tool to build the related domain ontology. Through the Karma modeling tool, the literature data set is mapped to the literature domain ontology and uniformly published as RDF data so that the semantic mapping is achieved, which effectively solve the important problem of multi-source and heterogeneous data. The Karma model that is built and published will be applied to complete big data set for big data integration. Finally, we sum up the results of the practice and address our future works.