Internal Data Imputation in Data Warehouse Dimensions

TL;DR

This paper introduces an internal data imputation method tailored for multidimensional data warehouse dimensions, leveraging existing data and relationships to address missing values efficiently for improved analysis.

Contribution

It presents a novel imputation approach specifically designed for dimension tables in data warehouses, considering intra- and inter-dimension relationships.

Findings

Effective imputation of missing dimension data

Reduces time and effort compared to existing methods

Enhances data completeness for better analysis

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.