Skip to main content

Comparative Evaluation of Large Data Model Representation Methods: The Analyst’s Perspective

  • Conference paper
  • First Online:
Conceptual Modeling — ER 2002 (ER 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2503))

Included in the following conference series:

Abstract

One of the most serious limitations of the Entity Relationship (ER) Model in practice is its inability to cope with complexity. A number of approaches have been proposed in the literature to address this problem, but so far there has been no systematic empirical research into the effectiveness of these methods. This paper describes a laboratory experiment which compares the effectiveness of different representation methods for documentation and maintenance of large data models (analyst’s viewpoint). The methods are compared using a range of performance-based and perception-based variables, including time taken, documentation correctness, consistency, perceived ease of use, perceived usefulness and intention to use. An important theoretical contribution of this paper is the development and empirical testing of a theoretical model (the Method Evaluation Model) for evaluating IS design methods. This model may help to bridge the gap between research and practice in IS design research, as it addresses the issue of method adoption in practice, which has largely been ignored by IS design researchers.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Akoka, J. and I. Comyn-Wattiau (1996): Entity Relationship And Object Oriented Model Automatic Clustering, Data And Knowledge Engineering, 20(1).

    Google Scholar 

  2. Allworth, S. (1996): Using Classification Structures To Develop And Structure Generic Industry Models, In D.L. Moody (Ed.) Proceedings Of The First Australian Data Management Conference, Melbourne, Australia: Australian Data Management Association (DAMA), December 2–3.

    Google Scholar 

  3. Allworth, S.(1999): Classification Structures Encourage the Growth of Generic Industry Models, In D.L. Moody (Ed.) Proceedings of the Eighteenth International Conference on Conceptual Modelling (Industrial Track), Paris, France: Springer, November 15–18.

    Google Scholar 

  4. Babbie, E.R. (1998): The Practice of Social Research, Belmont, California: Wadsworth Publishing.

    Google Scholar 

  5. Baskerville, R.L. and T. Wood-Harper (1996): A Critical Perspective on Action Research as a Method for Information Systems Research, Journal of Information Technology, 3(11): p. 235–246.

    Article  Google Scholar 

  6. Batra, D., J.A. Hoffer, and R.P. Bostrom (1990): Comparing Representations with Relational and EER Models, Communications of the ACM, 33(2), February: p. 126–139.

    Google Scholar 

  7. Bock, D.B. and T. Ryan (1993): Accuracy in Modelling with Extended Entity Relationship and Object Oriented Data Model, Journal Of Database Management, 4(4): p. 30–39.

    Google Scholar 

  8. Bodart, F., A. Patel, M. Sim, and R. Weber (2001): Should the Optional Property Construct be used in Conceptual Modelling: A Theory and Three Empirical Tests, Information Systems Research, 12(4).

    Google Scholar 

  9. Bubenko, J.A. (1986): Information Systems Methodologies-A Research View, In T.W. Olle, H.G. Sol, and A.A. Verrijn-Stuart (Eds.), Information Systems Design Methodologies: Improving The Practice: North-Holland

    Google Scholar 

  10. Chau, P.Y.K. (1996): An Empirical Assessment of a Modified Technology Acceptance Model, Journal of Management Information Systems, 13(2).

    Google Scholar 

  11. Conover, W.J. (1980): Practical Nonparametric Statistics (2nd edition), New York: John Wiley & Sons.

    Google Scholar 

  12. Davis, F.D., R.P. Bagozzi, and P.R. Warshaw (1989): User Acceptance of Computer Technology: A Comparison of Two Theoretical Models, Management Science, 35(8): p. 982–1003.

    Article  Google Scholar 

  13. Feldman, P. and D. Miller (1986): Entity Model Clustering: Structuring A Data Model By Abstraction, The Computer Journal, 29(4).

    Google Scholar 

  14. Fishbein, M. and I. Ajzen (1975): Belief, Attitude, Intention and Behaviour: An Introduction to Theory and Research, Reading, MA: Addison-Wesley.

    Google Scholar 

  15. Fitzgerald, G. (1991): Validating New Information Systems Techniques: A Retrospective Analysis, In H.E. Nissen, H.K. Klein, and R. Hirschheim (Eds.), Information Systems Research: Contemporary Approaches And Emergent Traditions: North-Holland

    Google Scholar 

  16. Galliers, R.D. (1991): Choosing Information Systems Research Approaches, In H.E. Nissen, H.K. Klein, and R. Hirschheim (Eds.), Information Systems Research: Contemporary Approaches And Emergent Traditions: North-Holland

    Google Scholar 

  17. Galliers, R.D. (1992): Information Systems Research: Issues, Methods and Practical Guidelines: Blackwell Scientific Publications.

    Google Scholar 

  18. Gandhi, M., EX. Robertson, and D. Van Gucht (1994): Levelled Entity Relationship Models, In P. Loucopolous (Ed.) Proceedings Of The Thirteenth International Conference On The Entity Relationship Approach, Manchester, December 14–17.

    Google Scholar 

  19. Gibbons, M., C. Limoges, H. Nowotny, S. Schwartzman, P. Scott, and M. Trow (1994): The New Production of Knowledge: The Dynamics of Science and Research in Contemporary Societies: Sage Publications.

    Google Scholar 

  20. Gilberg, R.F. (1986): A Schema Methodology For Large Entity-Relationship Diagrams, In P.P. Chen (Ed.) Proceedings Of Fourth International Conference On The Entity Relationship Approach

    Google Scholar 

  21. Hardgrave, B.C. and N.P. Dalal (1995): Comparing Object Oriented and Extended Entity Relationship Models, Journal Of Database Management, 6(3).

    Google Scholar 

  22. Hu, P.J. and P.Y.K. Chau (1999): Examining the Technology Acceptance Model Using Physician Acceptance of Telemedicine Technology, Journal of Management Information Systems, 16(2), Fall: p. 91–113.

    Google Scholar 

  23. Lee, H. and B.G. Choi (1998): A Comparative Study of Conceptual Data Modelling Techniques, Journal Of Database Management, 9(2), Spring: p. 26–35.

    Google Scholar 

  24. Maier, R. (1996): Benefits And Quality Of Data Modelling-Results Of An Empirical Analysis, In B. Thalheim (Ed.) Proceedings Of The Fifteenth International Conference On The Entity Relationship Approach, Cottbus, Germany: Elselvier, October 7–9.

    Google Scholar 

  25. Martin, J. and C.L. McClure (1985): Diagramming Techniques for Analysts and Programmers, Englewood Cliffs, N.J.: Prentice-Hall, xvi, 396.

    Google Scholar 

  26. Mathieson, K. (1991): Predicting User Intention: Comparing the Technology Acceptance Model with the Theory of Planned Behaviour, Information Systems Research, 2(3), September: p. 173–191.

    Google Scholar 

  27. Moody, D.L. (1997): A Multi-Level Architecture For Representing Enterprise Data Models, In D.W. Embley and R.C. Goldstein (Eds.), Proceedings Of The Sixteenth International Conference On Conceptual Modelling (ER’97), Los Angeles, November 1–3.

    Google Scholar 

  28. Moody, D.L. (2001): Dealing with Complexity: A Practical Method for Representing Large Entity Relationship Models (PhD Thesis), Melbourne, Australia: Department Of Information Systems, University of Melbourne.

    Google Scholar 

  29. Moody, D.L. (2002): Validation of a Method for Representing Large Entity Relationship Models: An Action Research Study, In Proceedings of the Tenth European Conference on Information Systems (ECIS’2002), Gdansk, Poland, June 6–8.

    Google Scholar 

  30. Moody, D.L. (2002): Complexity Effects On End User Understanding Of Data Models: An Experimental Comparison Of Large Data Model Representation Methods, In Proceedings of the Tenth European Conference on Information Systems (ECIS’2002), Gdansk, Poland, June 6–8.

    Google Scholar 

  31. Nordbotten, J.C. and M.E. Crosby (1999): The Effect of Graphic Style on Data Model Interpretation, Information Systems Journal, 9.

    Google Scholar 

  32. Nunally, J. (1978): Psychometric Theory (2nd edition), New York: McGraw Hill.

    Google Scholar 

  33. Nunamaker, J., M. Chen, and T.D.M. Purdin (1991): Systems Development in Information Systems Research, Journal of Management Information Systems, 7(3), Winter: p. 89–106.

    Google Scholar 

  34. Rescher, N. (1977): Methodological Pragmatism: Systems-Theoretic Approach to the Theory of Knowledge, Oxford: Basil Blackwell.

    Google Scholar 

  35. Shanks, G., A. Rouse, and D. Arnott (1993): A Review of Approaches to Research and Scholarship in Information Systems, In Proceedings of the 4th Australian Conference on Information Systems, Brisbane

    Google Scholar 

  36. Shanks, G.G. (1996): Building And Using Corporate Data Models (PhD Thesis), Melbourne, Australia: Department Of Information Systems, Monash University.

    Google Scholar 

  37. Sheppard, B.H., J. Harwick, and P.R. Warshaw (1988): The Theory of Reasoned Action: A Meta-Analysis of Past Research with Recommendation for Modification and Future Research, Journal of Consumer Research, 15(3), December: p. 325–343.

    Google Scholar 

  38. Shoval, P. and M. Even-Chaime (1987): Database Schema Design: An Experimental Comparison Between Normalisation and Information Analysis, Database, 18(3), Spring: p. 30–39.

    Article  Google Scholar 

  39. Shoval, P. and S. Shiran (1997): Entity-Relationship and Object-Oriented Data Modeling: An Experimental Comparison of Design Quality, Data & Knowledge Engineering, 21: p. 297–315.

    Article  MATH  Google Scholar 

  40. Simsion, G.C. (1989): A Structured Approach To Data Modelling, The Australian Computer Journal, August.

    Google Scholar 

  41. Teory, T.J., G. Wei, D.L. Bolton, and J.A. Koenig (1989): ER Model Clustering As An Aid For User Communication And Documentation In Database Design, Communications Of The ACM, August.

    Google Scholar 

  42. Thalheim, B. (1999): The Strength of ER Modelling, In Current Issues in Conceptual Modelling, P.P. Chen, et al. (Eds.), Springer-Verlag (Lecture Notes in Computer Science).

    Google Scholar 

  43. Wand, Y. and R.A. Weber (1993): On the Ontological Expressiveness of Information Systems Analysis and Design Grammars, Journal of Information Systems, October.

    Google Scholar 

  44. Weber, R.A. (1996): Are Attributes Entities? A Study Of Database Designers’ Memory Structures, Information Systems Research, June.

    Google Scholar 

  45. Weber, R.A. (1997): Ontological Foundations Of Information Systems, Melbourne, Australia:Coopers And Lybrand Accounting Research Methodology Monograph No. 4, Coopers And Lybrand.

    Google Scholar 

  46. Wynekoop, J.L. and NX. Russo (1997): Studying Systems Development Methodologies: An Examination Of Research Methods, Information Systems Journal, 7(1), January.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Moody, D.L. (2002). Comparative Evaluation of Large Data Model Representation Methods: The Analyst’s Perspective. In: Spaccapietra, S., March, S.T., Kambayashi, Y. (eds) Conceptual Modeling — ER 2002. ER 2002. Lecture Notes in Computer Science, vol 2503. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45816-6_25

Download citation

  • DOI: https://doi.org/10.1007/3-540-45816-6_25

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44277-6

  • Online ISBN: 978-3-540-45816-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics