Understanding Quality in Conceptual Modeling
IEEE Software
Entity-Relationship Modeling: Foundations of Database Technology
Entity-Relationship Modeling: Foundations of Database Technology
Introduction to Algorithms
Towards a Deeper Understanding of Quality in Requirements Engineering
CAiSe '95 Proceedings of the 7th International Conference on Advanced Information Systems Engineering
What Makes a Good Data Model? Evaluating the Quality of Entity Relationship Models
ER '94 Proceedings of the13th International Conference on the Entity-Relationship Approach
Improving Quality in Conceptual Modelling by the Use of Schema Transformations
ER '96 Proceedings of the 15th International Conference on Conceptual Modeling
Improving the Quality of Entity Relationship Models - Experience in Research and Practice
ER '98 Proceedings of the 17th International Conference on Conceptual Modeling
Metrics for Evaluating the Quality of Entity Relationship Models
ER '98 Proceedings of the 17th International Conference on Conceptual Modeling
The Guidelines of Modeling - An Approach to Enhance the Quality in Information Models
ER '98 Proceedings of the 17th International Conference on Conceptual Modeling
Schema transformations and dependency preservation
AIC'07 Proceedings of the 7th Conference on 7th WSEAS International Conference on Applied Informatics and Communications - Volume 7
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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.