Comprehensive complexity metric for data warehouse multidimensional model understandability

• Published in: IET software Vol. 14; no. 3; pp. 275 - 282
• Main Authors: Gosain, Anjana, Singh, Jaspreeti
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.06.2020
• Subjects: complexity measure; comprehensive complexity metric; correlation regression; data warehouse development; data warehouse multidimensional model understandability; data warehouse quality; data warehouses; hierarchy levels; hierarchy types; multidimensional model complexity; multinomial regression; regression analysis; software metrics; software quality; Special Issue: Knowledge Discovery for Software Development (KDSD); statistical techniques; structural complexity; statistical techniques; data warehouse development; hierarchy levels; regression analysis; comprehensive complexity metric; complexity measure; software quality; multidimensional model complexity; multinomial regression; correlation regression; data warehouse quality; hierarchy types; structural complexity; data warehouse multidimensional model understandability; data warehouses; software metrics
• ISSN: 1751-8806; 1751-8814; 1751-8814
• DOI: 10.1049/iet-sen.2019.0150

Data warehouse quality can be determined during the initial phases of data warehouse development by quantifying the structural complexity of multidimensional models using metrics. The structural complexity of a multidimensional model is guided by its elements, types, and relationships among those elements. So far, most of the researchers have dealt with metrics based on various elements (facts, dimensions, dimensional hierarchies, and hierarchy levels) existing in these models. However, not much consideration is given to different types of dimensions based on hierarchy types and different relationships among those elements. Therefore, this work proposes a comprehensive complexity metric for measuring multidimensional model complexity by taking into account various elements, their types and the relationships among the elements at various levels of granularity in these models. The theoretical validation of the proposed metric using the property-based framework given by Briand et al. characterises it as a complexity measure. Furthermore, the empirical study, employing statistical techniques (correlation and multinomial regression), on 26 multidimensional models and 20 subjects proved that the authors’ proposed metric is strongly correlated with multidimensional model understandability. Hence, this metric can be considered as a good predictor for data warehouse multidimensional model understandability.