A Semi-Automatic Design Methodology for (Big) Data Warehouse Transforming Facts into Dimensions

A decision support system is used by decision makers for a long time. But, in some cases, the originally designed multidimensional schema does not cover the entire needs of decision makers, which can change over time. One such unfulfilled need, is using facts to describe dimension members. In this article, we propose a methodology to transform the constellation schema of a data warehouse by integrating factual data into a dimension. The proposed methodology and algorithms enrich a constellation multidimensional schema with new analytical possibilities for decision makers. This enrichment has repercussions for the entire multidimensional schema that are managed by multidimensional modeling, hierarchy calculation and the hierarchy version. In this article, we present a theoretical view of the proposed methodology supported by a case study, an implemented prototype and a complete evaluation based on a standard benchmark.

Automated summary

This article proposes a methodology to enrich a multidimensional schema by integrating factual data into dimensions to meet changing needs of decision makers, providing new analytical possibilities and impacting the entire schema managed by multidimensional modeling, hierarchy calculation, and versioning.