Experimental Validation of Multidimensional Data Models Metrics

Multidimensional data models are playing an increasingly prominent role in support of day-to-day business decisions. Due to their significance in taking strategic decisions it is fundamental to assure its quality. Although there are some useful guidelines proposals for designing multidimensional data models, objective indicators (metrics) are needed to help designers and managers to develop quality multidimensional data models. In this paper we present two metrics (Number of Fact Tables, NFT and Number of Dimensional Tables, NDT) we have defined for multidimensional data models and an experiment developed in order to validate them as quality indicators. As a result of this experiment it seems that the number of fact tables can be considered as a solid quality indicator of a multidimensional data model.

• Type: Conference
• Year: 2003
• Where: HICSS
• Authors: Manuel A. Serrano, Coral Calero, Mario Piattini
• Keywords: Biometrics, HICSS 2003, Multidimensional Data Model, Quality Indicator, Quality Multidimensional Data, System Sciences