{"title":"信息分析的组合几何方法及其在大数据中的应用","authors":"V. Vereshchaga, Y. Adoniev","doi":"10.33842/2313-125X/2021/20/68/75","DOIUrl":null,"url":null,"abstract":"The article proposes a composite geometric method for analysis of information in Big Data sets at the stage of their primary processing and “cleaning”. The method is based on the methods of the Baluba-Naydysh point calculus is a preparatory stage when using the structural geometric modelling of Big Data. the minimal use of machine resources when working with Big Data significantly reduces the cost of obtaining valuable conclusions and forecasts.","PeriodicalId":188754,"journal":{"name":"Modern problems of modeling","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"COMPOSITIONAL GEOMETRIC METHOD OF INFORMATION ANALYSIS AND ITS APPLICATION WHEN WORKING WITH BIG DATA\",\"authors\":\"V. Vereshchaga, Y. Adoniev\",\"doi\":\"10.33842/2313-125X/2021/20/68/75\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The article proposes a composite geometric method for analysis of information in Big Data sets at the stage of their primary processing and “cleaning”. The method is based on the methods of the Baluba-Naydysh point calculus is a preparatory stage when using the structural geometric modelling of Big Data. the minimal use of machine resources when working with Big Data significantly reduces the cost of obtaining valuable conclusions and forecasts.\",\"PeriodicalId\":188754,\"journal\":{\"name\":\"Modern problems of modeling\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-02-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Modern problems of modeling\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.33842/2313-125X/2021/20/68/75\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Modern problems of modeling","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.33842/2313-125X/2021/20/68/75","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
COMPOSITIONAL GEOMETRIC METHOD OF INFORMATION ANALYSIS AND ITS APPLICATION WHEN WORKING WITH BIG DATA
The article proposes a composite geometric method for analysis of information in Big Data sets at the stage of their primary processing and “cleaning”. The method is based on the methods of the Baluba-Naydysh point calculus is a preparatory stage when using the structural geometric modelling of Big Data. the minimal use of machine resources when working with Big Data significantly reduces the cost of obtaining valuable conclusions and forecasts.
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