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36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 9
Big Island, Hawaii
January 06-January 09
ISBN: 0-7695-1874-5
Manuel Serrano, University of Castilla — La Mancha
Coral Calero, University of Castilla — La Mancha
Mario Piattini, University of Castilla — La Mancha
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.
Citation:
Manuel Serrano, Coral Calero, Mario Piattini, "Experimental Validation of Multidimensional Data Models Metrics," hicss, vol. 9, pp.327b, 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 9, 2003
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