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Object Class or Association Class? Testing the User Effect on Cardinality Interpretation

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Perspectives in Conceptual Modeling (ER 2005)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3770))

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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.

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References

  1. 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)

    Article  Google Scholar 

  2. 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)

    Google Scholar 

  3. 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)

    Google Scholar 

  4. 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)

    Google Scholar 

  5. Connolly, T., Begg, C.: Database Systems: A Practical Approach to Design, Implementation, and Management, 3rd edn. Addison-Wesley, Reading (2002)

    Google Scholar 

  6. Date, C.J.: An Introduction to Database Systems, 6th edn. Addison-Wesley, Reading (1995)

    MATH  Google Scholar 

  7. Dedene, G., Snoeck, M.: Formal deadlock elimination in an object-oriented conceptual schema. Data & Knowledge Engineering 15(1), 1–30 (1995)

    Article  MATH  Google Scholar 

  8. 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)

    Google Scholar 

  9. 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)

    Article  Google Scholar 

  10. Gemino, A., Wand, Y.: Evaluating modeling techniques based on models of learning. Communications of the ACM 46(10), 79–84 (2003)

    Article  Google Scholar 

  11. 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)

    Chapter  Google Scholar 

  12. Halpin, T.: Conceptual Schema and Relational Database Design: A Fact Oriented Approach, 2nd edn. Prentice-Hall, Englewood Cliffs (1996)

    Google Scholar 

  13. Kim, Y.-G., March, S.T.: Comparing Data Modelling Formalisms. Communications of the ACM 38(6), 103–115 (1995)

    Article  Google Scholar 

  14. OMG, UML 2.0 Superstructure Specification, Revised Final Adopted Specification (October 8, 2004), Object Management Group (2004), http://www.omg.org

  15. 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)

    Google Scholar 

  16. 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)

    Google Scholar 

  17. 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

    Google Scholar 

  18. 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)

    Google Scholar 

  19. Shanks, G., Tansley, E., Weber, R.: Using ontology to validate conceptual models. Communications of the ACM 46(10), 85–89 (2003)

    Article  Google Scholar 

  20. 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)

    Article  Google Scholar 

  21. Stevens, P.: On the interpretation of binary associations in the Unified Modeling Language. Software and Systems Modeling 1(1), 68–79 (2002)

    Article  Google Scholar 

  22. 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)

    Article  Google Scholar 

  23. Vessey, I.: Cognitive fit: A theory-based analysis of the graph versus tables literature. Decision Sciences 22(2), 219–240 (1991)

    Article  Google Scholar 

  24. 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)

    Article  Google Scholar 

  25. Wand, Y., Weber, R.: Information Systems and Conceptual Modeling – A Research Agenda. Information Systems Research 13(4), 363–376 (2002)

    Article  Google Scholar 

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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

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  • 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)

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