{"title":"“Set of Strings”大数据建模框架","authors":"I. Sheremet","doi":"10.5772/INTECHOPEN.85602","DOIUrl":null,"url":null,"abstract":"The most complicated task for big data modeling in comparison with relational approach is its variety, being a consequence of heterogeneity of sources of data, accumulated in the integrated storage space. “Set of Strings” Framework (SSF) provides unified solution of this task by representation of database as updated finite set of facts, being strings, in which structure is defined by current metadatabase, which is also an updated set of the context-free generating rules. This chapter is dedicated to SSF formal and substantial description.","PeriodicalId":224487,"journal":{"name":"Introduction to Data Science and Machine Learning","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"“Set of Strings” Framework for Big Data Modeling\",\"authors\":\"I. Sheremet\",\"doi\":\"10.5772/INTECHOPEN.85602\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The most complicated task for big data modeling in comparison with relational approach is its variety, being a consequence of heterogeneity of sources of data, accumulated in the integrated storage space. “Set of Strings” Framework (SSF) provides unified solution of this task by representation of database as updated finite set of facts, being strings, in which structure is defined by current metadatabase, which is also an updated set of the context-free generating rules. This chapter is dedicated to SSF formal and substantial description.\",\"PeriodicalId\":224487,\"journal\":{\"name\":\"Introduction to Data Science and Machine Learning\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-04-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Introduction to Data Science and Machine Learning\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5772/INTECHOPEN.85602\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Introduction to Data Science and Machine Learning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5772/INTECHOPEN.85602","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The most complicated task for big data modeling in comparison with relational approach is its variety, being a consequence of heterogeneity of sources of data, accumulated in the integrated storage space. “Set of Strings” Framework (SSF) provides unified solution of this task by representation of database as updated finite set of facts, being strings, in which structure is defined by current metadatabase, which is also an updated set of the context-free generating rules. This chapter is dedicated to SSF formal and substantial description.
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