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dc.contributor.authorRorimpandey, Gladly Caren-
dc.contributor.authorSangkop, Ferdinan Ivan-
dc.contributor.authorRantung, Vivi Peggie-
dc.contributor.authorZwart, J P-
dc.contributor.authorLiando, Olivia Eunike Selvie-
dc.contributor.authorMewengkang, Alfrina-
dc.date.accessioned2018-03-21T01:16:12Z-
dc.date.available2018-03-21T01:16:12Z-
dc.date.issued2018-02-22-
dc.identifier.citationG C Rorimpandey et al 2018 IOP Conf. Ser.: Mater. Sci. Eng. 306 012044en_US
dc.identifier.otherdoi:10.1088/1757-899X/306/1/012044-
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/35-
dc.description.abstractData Warehouses have increasingly become important in organizations that have large amount of data. It is not a product but a part of a solution for the decision support system in those organizations. Data model is the starting point for designing and developing of data warehouses architectures. Thus, the data model needs stable interfaces and consistent for a longer period of time. The aim of this research is to know which data model in data warehousing has the best performance. The research method is descriptive analysis, which has 3 main tasks, such as data collection and organization, analysis of data and interpretation of data. The result of this research is discussed in a statistic analysis method, represents that there is no statistical difference among data models used in data warehousing. The organization can utilize four data model proposed when designing and developing data warehouse.en_US
dc.language.isoenen_US
dc.publisherIOP Publishing Ltden_US
dc.relation.ispartofseriesIOP Conf. Series: Materials Science and Engineering;306-
dc.titleData Model Performance in Data Warehousingen_US
dc.typeArticleen_US
Appears in Collections:Lecturer Scientific Papers

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