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Data & Knowledge Engineering
Volume 55, Issue 3, December 2005, Pages 301-326
Quality in conceptual modeling - Five examples of the state of art
 
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doi:10.1016/j.datak.2004.12.009    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2005 Elsevier B.V. All rights reserved.

Complexity and clarity in conceptual modeling: Comparison of mandatory and optional properties

Andrew Geminoa, Corresponding Author Contact Information, E-mail The Corresponding Author and Yair Wandb, 1, E-mail The Corresponding Author

aFaculty of Business Administration, Simon Fraser University, 8888 University Drive, Burnaby, BC, Canada V5A 1S6 bSauder School of Business, University of British Columbia, Vancouver, BC, Canada V6T 1Z2

Received 14 December 2004; 
accepted 14 December 2004. 
Available online 25 February 2005.

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Abstract

Two versions of the entity-relationship model (ERM) are compared in this empirical study. One model grammar uses optional properties and the other employs mandatory properties and subtypes. The optional grammar produces apparently less complex models than the mandatory with subtypes. An ontological analysis indicates that mandatory properties may be superior to optional properties in providing clearer representations. The Cognitive Theory of Multimedia Learning is used to hypothesize superior local information provided by mandatory properties can lead to improved viewer understanding of a model. An experiment comparing the two ERM grammars is described and results confirm the use of mandatory relationships leads to improved understanding even though the model is apparently more complex. These results suggest clarity within the model may be more important than the apparent complexity of the model when a model is used for developing domain understanding.

Keywords: Conceptual modeling; Systems analysis; Ontology; Cognitive theory; The entity-relationship model

Article Outline

1. Introduction
1.1. Organization of the paper
2. Comparing modeling grammars
3. Ontological analysis
4. Cognitive considerations
4.1. A cognitive perspective of conceptual models
4.2. Elements involved when using models to communicate
4.3. Cognitive processing of model information
4.4. Outcomes and measurement
5. Hypotheses
6. Method
6.1. Additional independent and control variables
6.2. Participants
6.3. Materials
6.4. Design
7. Results
7.1. Other factors considered
7.2. Summary of results
8. Conclusions
Acknowledgements
Appendix A. Experimental materials
A.1. Description for Voyager bus case
A.2. Pretest questions
A.3. Post test questions: ease of interpretation
A.4. Comprehension questions and answers
A.5. Problem solving questions and examples for acceptable answers: Voyager bus case
A.6. Cloze test: example from Voyager bus
References
Vitae





Data & Knowledge Engineering
Volume 55, Issue 3, December 2005, Pages 301-326
Quality in conceptual modeling - Five examples of the state of art
 
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