Jens Bleiholder, Sascha Szott, Melanie Herschel, Felix Naumann
{"title":"数据集成的补并","authors":"Jens Bleiholder, Sascha Szott, Melanie Herschel, Felix Naumann","doi":"10.1109/ICDEW.2010.5452760","DOIUrl":null,"url":null,"abstract":"A data integration process consists of mapping source data into a target representation (schema mapping [1]), identifying multiple representations of the same real-word object (duplicate detection [2]), and finally combining these representations into a single consistent representation (data fusion [3]). Clearly, as multiple representations of an object are generally not exactly equal, during data fusion, we have to take special care in handling data conflicts. This paper focuses on the definition and implementation of complement union, an operator that defines a new semantics for data fusion.","PeriodicalId":442345,"journal":{"name":"2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Complement union for data integration\",\"authors\":\"Jens Bleiholder, Sascha Szott, Melanie Herschel, Felix Naumann\",\"doi\":\"10.1109/ICDEW.2010.5452760\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A data integration process consists of mapping source data into a target representation (schema mapping [1]), identifying multiple representations of the same real-word object (duplicate detection [2]), and finally combining these representations into a single consistent representation (data fusion [3]). Clearly, as multiple representations of an object are generally not exactly equal, during data fusion, we have to take special care in handling data conflicts. This paper focuses on the definition and implementation of complement union, an operator that defines a new semantics for data fusion.\",\"PeriodicalId\":442345,\"journal\":{\"name\":\"2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010)\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDEW.2010.5452760\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDEW.2010.5452760","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A data integration process consists of mapping source data into a target representation (schema mapping [1]), identifying multiple representations of the same real-word object (duplicate detection [2]), and finally combining these representations into a single consistent representation (data fusion [3]). Clearly, as multiple representations of an object are generally not exactly equal, during data fusion, we have to take special care in handling data conflicts. This paper focuses on the definition and implementation of complement union, an operator that defines a new semantics for data fusion.