Authors: Mian M. Awais, Shafay Shamail, Tauqeer Hussain
Tags: 2005, conceptual modeling
Completeness is one of the important measures for semantic quality of a conceptual model, an ER model in our case. In this paper, a complete methodology is presented to measure completeness quantitatively. This methodology identifies existence of functional dependencies in the given conceptual model and transforms it into a multi-graph using the transformation rules proposed in this paper. This conversion can be helpful in implementing and automating computation of quality metrics for a given conceptual model. The new Fuzzy Completeness Index (FCI) introduced in this paper adopts an improved approach over Completeness Index proposed by authors in the previous research. FCI takes into account the extent a functional dependency has its representation in the conceptual model even when it is not fully represented. This partial representation of a functional dependency is measured using the fuzzy membership values and fuzzy hedges. The value of FCI varies between 0 and 1, where 1 represents a model that incorporates all the functional dependencies associated with it. Computation of FCI is demonstrated for a number of conceptual models. It is illustrated that the quality in terms of completeness can effectively be measured and compared through the FCI based approach.Read the full paper here: https://link.springer.com/chapter/10.1007/11568346_44
