Empirical investigation of dimension hierarchy sharing-based metrics for multidimensional schema understandability

Abstract

Over the last years, quality has gained lot of importance in the development of data warehouse systems. Predicting understandability of multidimensional schemas could play a key role in controlling data warehouse quality at early stages of development. In this area, some effort has been spent to define structural metrics and identity models for assessing quality of these systems. Of the structural properties used to define metrics, aspects of dimension hierarchies and its sharing plays primary role to enhance analytical capabilities of multidimensional schemas, thereby affecting their quality. The authors have previously proposed structural metrics based on aforementioned aspects. The objective of this study is to apply principal component analysis (PCA) to find whether our metrics are improvements over the other existing metrics; and to apply logistic regression to study whether the metrics (selected as relevant in the extracted principal components) combined together are indicators of multidimensional schema understandability. The results of PCA confirm that our structural metrics based on the concept of sharing are different from other such metrics existing in the literature. Further, the metrics selected as principal components can be used in combination to predict understandability of data warehouse multidimensional schemas.