Abstract
In UML class diagrams, a many-to-many relationship with attributes can be represented by an association class or by a connecting object class. It is unclear which modeling construct is preferred in particular modeling scenarios. Because of lack of theory, this paper investigates the issue empirically. An experiment was conducted that tested the effect of representational form chosen on the performance of model users at cardinality interpretation tasks. It was shown that, controlling for cardinality knowledge, business users can better interpret the information that a UML class diagram conveys about a many-to-many relationship with attributes if this relationship is represented as an association class. The implication for ‘best practices’ in UML modeling is that modelers should refrain from objectifying such relationships if the goal is an effective communication of domain semantics to users that are not modeling experts.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Batra, D., Wishart, N.A.: Comparing a rule-based approach with a pattern-based approach at different levels of complexity of conceptual data modelling tasks. International Journal of Human-Computer Studies 61, 397–419 (2004)
Bowen, P.L., O’Farrel, R.A., Rohde, F.H.: How Does Your Model Grow? An Empirical Investigation of the Effects of Ontological Clarity and Application Domain Size on Query Performance. In: Proceedings of the 25th International Conference on Information Systems, Washington DC, USA, pp. 77–90 (2004)
Burton-Jones, A., Meso, P.: How good are these UML diagrams. An empirical test of the Wand and Weber good decomposition model. In: 23rd International Conference on Information Systems, Barcelona, Spain, pp. 101–114 (2002)
Burton-Jones, A., Weber, R.: nderstanding relationships with attributes in entity-relationship diagrams. In: Proceedings of the 20th International Conference on Information Systems, Charlotte, NC, USA, pp. 214–228 (1999)
Connolly, T., Begg, C.: Database Systems: A Practical Approach to Design, Implementation, and Management, 3rd edn. Addison-Wesley, Reading (2002)
Date, C.J.: An Introduction to Database Systems, 6th edn. Addison-Wesley, Reading (1995)
Dedene, G., Snoeck, M.: Formal deadlock elimination in an object-oriented conceptual schema. Data & Knowledge Engineering 15(1), 1–30 (1995)
Dunn, C.L., Gerard, G.J., Grabski, S.V.: Visual Attention Overload: Representation Effects on Cardinality Error Identification. In: Proceedings of the 24th International Conference on Information Systems, Seattle, WA, USA, pp. 47–58 (2003)
Dunn, C.L., Grabski, S.V.: An Investigation of Localization as an Element of Cognitive Fit in Accounting Model Representations. Decision Sciences 32(1), 55–94 (2001)
Gemino, A., Wand, Y.: Evaluating modeling techniques based on models of learning. Communications of the ACM 46(10), 79–84 (2003)
Genero, M., Poels, G., Piattini, M.: Defining and Validating Measures for Conceptual Data Model Quality. In: Hernández, J., Moreira, A. (eds.) ECOOP-WS 2002. LNCS, vol. 2548, pp. 147–153. Springer, Heidelberg (2002)
Halpin, T.: Conceptual Schema and Relational Database Design: A Fact Oriented Approach, 2nd edn. Prentice-Hall, Englewood Cliffs (1996)
Kim, Y.-G., March, S.T.: Comparing Data Modelling Formalisms. Communications of the ACM 38(6), 103–115 (1995)
OMG, UML 2.0 Superstructure Specification, Revised Final Adopted Specification (October 8, 2004), Object Management Group (2004), http://www.omg.org
Parsons, J.: Effects of Local Versus Global Schema Diagrams on Verification and Communication in Conceptual Data Modelling. Journal of Management Information Systems 19(3), 155–183 (2003)
Parsons, J., Cole, L.: An Experimental Examination of Property Precedence in Conceptual Modelling. In: Proceedings of the 1st Asia-Pacific Conference on Conceptual Modeling, Dunedin, New Zealand (2004)
Parsons, J., Cole, L.: What Do the Pictures Mean? Guidelines for Experimental Evaluation of Representation Fidelity in Diagrammatic Conceptual Modeling Techniques. Data & Knowledge Engineering (2005) accepted for publication
Schütte, R., Rotthowe, T.: The Guidelines of Modeling – An Approach to Enhance the Quality in Information Models. In: Ling, T.-W., Ram, S., Li Lee, M. (eds.) ER 1998. LNCS, vol. 1507, pp. 240–254. Springer, Heidelberg (1998)
Shanks, G., Tansley, E., Weber, R.: Using ontology to validate conceptual models. Communications of the ACM 46(10), 85–89 (2003)
Snoeck, M., Dedene, G.: Existence Dependency: The key to semantic integrity between structural and behavioural aspects of object types. IEEE Transactions on Software Engineering 24(4), 231–253 (1998)
Stevens, P.: On the interpretation of binary associations in the Unified Modeling Language. Software and Systems Modeling 1(1), 68–79 (2002)
Topi, H., Ramesh, V.: Human Factors Research on Data Modeling: A Review of Prior Research, An Extended Framework and Future Research Directions. Journal of Database Management 13(2), 3–19 (2002)
Vessey, I.: Cognitive fit: A theory-based analysis of the graph versus tables literature. Decision Sciences 22(2), 219–240 (1991)
Wand, Y., Storey, V.C., Weber, R.: An Ontological Analysis of the Relationship Construct in Conceptual Modeling. ACM Transactions on Database Systems 24(4), 494–528 (1999)
Wand, Y., Weber, R.: Information Systems and Conceptual Modeling – A Research Agenda. Information Systems Research 13(4), 363–376 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Poels, G., Gailly, F., Maes, A., Paemeleire, R. (2005). Object Class or Association Class? Testing the User Effect on Cardinality Interpretation. In: Akoka, J., et al. Perspectives in Conceptual Modeling. ER 2005. Lecture Notes in Computer Science, vol 3770. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11568346_5
Download citation
DOI: https://doi.org/10.1007/11568346_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29395-8
Online ISBN: 978-3-540-32239-9
eBook Packages: Computer ScienceComputer Science (R0)