Authors: Anjana Gosain; Sangeeta Sabharwal; Sushama Nagpal
Addresses: University School of Information Technology, GGSIPU, New Delhi-110006, India. ' Computer Engineering Division, Netaji Subhas Institute of Technology, Dwarka, New Delhi-110078, India. ' Computer Engineering Division, Netaji Subhas Institute of Technology, Dwarka, New Delhi-110078, India
Abstract: Data warehouses are large repositories designed to enable the knowledge workers to take better and faster decisions. Due to its significance in strategic decisions, there is a need to assure data warehouse quality. One of the factors affecting the data warehouse quality is multidimensional model quality. Although there are some useful guidelines for designing good multidimensional data models, but objective indicators, i.e., metrics are needed to help designers to develop quality multidimensional models. Few researchers have proposed quality metrics for multidimensional models for data warehouse. These metrics need to be theoretically as well as empirically validated in order to prove their practical utility. In this paper, empirical validation using controlled experiment is carried out. We not only evaluate the effect of individual metric but also evaluate the effect of various combinations of metrics on data warehouse model quality specifically understandability, in order to best explain the variance of dependent variable due to independent variables. The results show that these metrics may be used as objective indicators for understandability. Finally, accuracy of our model in predicting the multidimensional model quality is also evaluated.
Keywords: data warehouses; multidimensional models; metrics; empirical validation; data warehouse quality; information quality; model quality.
International Journal of Information Quality, 2011 Vol.2 No.4, pp.344 - 358
Accepted: 27 Jul 2011
Published online: 21 Nov 2011 *