Article,
Applying fuzzy logic to measure completeness of a conceptual model
Applied Mathematics and Computation, 185 (2): 1078--1086 (2007)Special Issue on Intelligent Computing Theory and Methodology.
DOI: 10.1016/j.amc.2006.07.053
Abstract
In a computing environment, the success of an information system depends upon the quality of its conceptual model. The importance of measuring quality of a conceptual model in quantitative terms has been emphasized in the research but still the quantitative measures are very scarce in the literature. A new Fuzzy Completeness Index (FCI) is introduced in this paper as a quantitative measure for the quality of a conceptual model. It takes into account completeness of a conceptual model based upon the concept of functional dependencies. For a given conceptual model the incorporation of functional dependencies is mapped onto a TAS Graph and is then measured using the fuzzy membership values and fuzzy hedges. The FCI has been calculated for different conceptual models. It has been illustrated that the quality in terms of completeness can effectively be measured through the FCI based approach. The higher the value of FCI the closer is the conceptual model to the real world in representing functional constraints.
