Empirical investigation of metrics for multidimensional model of Data Warehouse using Support Vector Machine

Data Warehouse is the backbone of all analytics oriented organizations where business decisions need to be taken. Due to its role as a decision support system, its quality becomes crucial. Data warehouse conceptual models can be used to determine its quality during the early stages of design. Several metrics have been proposed to estimate the quality of these models. In order to corroborate the practical applicability of these metrics, it is important to validate them empirically. A number of propositions have been made in the past for the empirical validation of these metrics largely using statistical techniques of correlation and regression. However, statistical techniques are unable to model complex and non-linear relationships between the metrics and quality of the data warehouse models. In this paper, we have made an attempt to assess the non-linear relationship between the data warehouse structural metrics and understandability of its models by using Support Vector Machine (SVM). The results indicate that the proposed SVM model may aid in determining the understandability and inturn quality of the data warehouse conceptual models with high accuracy.