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  • 标题:Conceptual model of operational–analytical data marts for big data processing
  • 本地全文:下载
  • 作者:Aleksey Raevich ; Boris Dobronets ; Olga Popova
  • 期刊名称:E3S Web of Conferences
  • 印刷版ISSN:2267-1242
  • 电子版ISSN:2267-1242
  • 出版年度:2020
  • 卷号:149
  • 页码:1-6
  • DOI:10.1051/e3sconf/202014902011
  • 出版社:EDP Sciences
  • 摘要:Operational data marts that basically constitute slices of thematic narrowly-focused information are designed to provide operational access to big data sources due to consolidation and ranking of information resources based on their relevance. Unlike operational data marts dependent on the sources, analytical data marts are considered as independent data sources created by users to provide structuring of data for the tasks being solved. Therefore, the conceptual model of operational-analytical data marts allows combining the concepts of operational and analytical data marts to generate an analytical cluster that shall act as the basis for quick designing, development and implementation of data models.
  • 其他摘要:Operational data marts that basically constitute slices of thematic narrowly-focused information are designed to provide operational access to big data sources due to consolidation and ranking of information resources based on their relevance. Unlike operational data marts dependent on the sources, analytical data marts are considered as independent data sources created by users to provide structuring of data for the tasks being solved. Therefore, the conceptual model of operational-analytical data marts allows combining the concepts of operational and analytical data marts to generate an analytical cluster that shall act as the basis for quick designing, development and implementation of data models.
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