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Internal Data Imputation in Data Warehouse Dimensions

Abstract : Missing values occur commonly in the multidimensional data warehouses. They may generate problems of usefulness of data since the analysis performed on a multidimensional data warehouse is through different dimensions with hierarchies where we can roll up or drill down to the different parameters of analysis. Therefore, it's essential to complete these missing values in order to carry out a better analysis. There are existing data imputation methods which are suitable for numeric data, so they can be applied for fact tables but not for dimension tables. Some other data imputation methods need extra time and effort costs. As consequence, we propose in this article an internal data imputation method for multidimensional data warehouse based on the existing data and considering the intra-dimension and inter-dimension relationships.
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Contributor : Jérôme Darmont Connect in order to contact the contributor
Submitted on : Friday, October 1, 2021 - 7:28:30 PM
Last modification on : Wednesday, June 1, 2022 - 4:29:19 AM
Long-term archiving on: : Sunday, January 2, 2022 - 7:58:15 PM


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yuzhao yang, Fatma Abdelhedi, Jérôme Darmont, Franck Ravat, Olivier Teste. Internal Data Imputation in Data Warehouse Dimensions. 32nd International Conference on Database and Expert Systems Applications (DEXA 2021), Sep 2021, Linz, Austria. pp.237-244, ⟨10.1007/978-3-030-86472-9_22⟩. ⟨hal-03265060⟩



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