{"title":"用数据库建模数据湖元数据","authors":"I. D. Nogueira, Maram Romdhane, J. Darmont","doi":"10.1145/3216122.3216130","DOIUrl":null,"url":null,"abstract":"With the rise of big data, business intelligence had to find solutions for managing even greater data volumes and variety than in data warehouses, which proved ill-adapted. Data lakes answer these needs from a storage point of view, but require managing adequate metadata to guarantee an efficient access to data. Starting from a multidimensional metadata model designed for an industrial heritage data lake presenting a lack of schema evolutivity, we propose in this paper to use ensemble modeling, and more precisely a data vault, to address this issue. To illustrate the feasibility of this approach, we instantiate our metadata conceptual model into relational and document-oriented logical and physical models, respectively. We also compare the physical models in terms of metadata storage and query response time.","PeriodicalId":422509,"journal":{"name":"Proceedings of the 22nd International Database Engineering & Applications Symposium","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":"{\"title\":\"Modeling Data Lake Metadata with a Data Vault\",\"authors\":\"I. D. Nogueira, Maram Romdhane, J. Darmont\",\"doi\":\"10.1145/3216122.3216130\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the rise of big data, business intelligence had to find solutions for managing even greater data volumes and variety than in data warehouses, which proved ill-adapted. Data lakes answer these needs from a storage point of view, but require managing adequate metadata to guarantee an efficient access to data. Starting from a multidimensional metadata model designed for an industrial heritage data lake presenting a lack of schema evolutivity, we propose in this paper to use ensemble modeling, and more precisely a data vault, to address this issue. To illustrate the feasibility of this approach, we instantiate our metadata conceptual model into relational and document-oriented logical and physical models, respectively. We also compare the physical models in terms of metadata storage and query response time.\",\"PeriodicalId\":422509,\"journal\":{\"name\":\"Proceedings of the 22nd International Database Engineering & Applications Symposium\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"20\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 22nd International Database Engineering & Applications Symposium\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3216122.3216130\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 22nd International Database Engineering & Applications Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3216122.3216130","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
With the rise of big data, business intelligence had to find solutions for managing even greater data volumes and variety than in data warehouses, which proved ill-adapted. Data lakes answer these needs from a storage point of view, but require managing adequate metadata to guarantee an efficient access to data. Starting from a multidimensional metadata model designed for an industrial heritage data lake presenting a lack of schema evolutivity, we propose in this paper to use ensemble modeling, and more precisely a data vault, to address this issue. To illustrate the feasibility of this approach, we instantiate our metadata conceptual model into relational and document-oriented logical and physical models, respectively. We also compare the physical models in terms of metadata storage and query response time.