Skip to main content
SpringerLink
Log in

Graphical Entity Relationship models: Towards a more user understandable representation of data

  • Session 6: Principles of Database Design
  • Conference paper
  • First Online:
Conceptual Modeling — ER '96 (ER 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1157))

Included in the following conference series:

Abstract

The Entity Relationship Model was originally proposed as a way of representing user requirements in a way that non-technical users could understand. However anecdotal evidence and empirical studies both indicate that users have major difficulties understanding Entity Relationship models in practice. This paper proposes a number of modifications to the Entity Relationship Model to make it more understandable to business users. These include the use of an enhanced graphical representation, levels of abstraction and the use of business scenarios. This method has been used successfully in a wide range of organisational contexts, and has been particularly successful at the corporate level, where understandability of models has been found to be a major barrier to their acceptance and use. In addition, an automated tool has been developed to support the technique, which allows users to interact directly with the model and understand how it works through the use of animation.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Batini, C., Ceri, S. and Navathe, S.B., “Conceptual Database Design: An Entity Relationship Approach”, Benjamin Cummings, Redwood City, California, 1992.

    Google Scholar 

  2. Berman, S.,“A Semantic Data Model As The Basis For An Automated Database Design Tool”, Information Systems, Vol 11, No. 2, 1986.

    Google Scholar 

  3. Brodie, M.L., Myopolous, J. and Schmidt, J.W. (Eds), On Conceptual Modelling, Springer-Verlag, New York, 1984.

    Google Scholar 

  4. Butler Cox Foundation, Requirements Definition: The Key to Systems Development Productivity, Position Paper No. 4, Butler Cox and Partners Limited, London, November, 1987.

    Google Scholar 

  5. Campbell, D.,“Entity Relationship Modelling: One Style Suits All”, Database, Summer, 1992.

    Google Scholar 

  6. Checkland, P.B. and Scholes, J., Soft Systems Methodology in Action, Wiley, Chichester, 1990.

    Google Scholar 

  7. Checkland, P.B., Systems Thinking, Systems Practice, Wiley, Chichester, 1981.

    Google Scholar 

  8. Chen, P.P., The Entity-Relationship Model: Towards a Unified View of Data, ACM Transactions on Database Systems, 1, 1976.

    Google Scholar 

  9. Date, C.J., Relational Database: Selected Writings, Addison-Wesley, 1986.

    Google Scholar 

  10. De Bono, Edward, De Bono's Thinking Course, BBC Books, London, 1989.

    Google Scholar 

  11. De Marco, T., Structured Analysis and System Specification, Yourdon Press, 1978.

    Google Scholar 

  12. Dumpala, S.R. and Arora, S.V., Schema translation using the Entity-Relationship Approach, In Chen, P.P. (ed.) The Entity-Relationship Approach to Information Modelling and Analysis, North-Holland, 1983.

    Google Scholar 

  13. Elmasri, R., and Navathe, S.B., “Fundamentals of Database Systems”, Benjamin Cummings, Redwood City, California, 1989.

    Google Scholar 

  14. Feldman, P. and Miller, D., Entity Model Clustering: Structuring a Data Model by Abstraction, The Computer Journal, Vol. 29, No. 4,1986.

    Google Scholar 

  15. Finklestein, C., “An Introduction to Information Engineering: From Strategic Planning to Information Systems”, Addison-Wesley, Singapore, 1989.

    Google Scholar 

  16. Flood, R.L. and Carson, E.R., Dealing With Complexity: An Introduction to the Theory and Application of Systems Science, Plenum Press, 1988.

    Google Scholar 

  17. Goldstein, R.C. and Storey, V.C., “Some Findings On The Intuitiveness of Entity Relationship Constructs”, In Entity Relationship Relationship Approach to Database Design and Querying”, Lochovsky, F.H. (ed.), Elsevier Science, Amsterdam, 1990.

    Google Scholar 

  18. Hawryszkiewycz, I.T., “Database Analysis and Design”, Science Research Associates, 1984.

    Google Scholar 

  19. Hitchman, S., “Practitioner Perceptions On The Use Of Some Semantic Concepts In The Entity Relationship Model”, European Journal of Information Systems, 4, 31–40, 1995.

    Google Scholar 

  20. Hull, R. and King, R. Semantic Data Models, ACM Computing Surveys, September, 1988.

    Google Scholar 

  21. ISO, Information Processing Systems: Concepts And Terminology For The Conceptual Schema And The Information Base. ISO Technical Report 9007, 1987.

    Google Scholar 

  22. Jacobson, I., “Object Oriented Systems Engineering”, Addison-Wesley, New York, 1992.

    Google Scholar 

  23. Jacobson, I., “The Object Advantage”, Addison-Wesley, New York, 1995.

    Google Scholar 

  24. Kent, W. The Realities Of Data: Basic Properties Of Data Reconsidered. In Steel, T.B. And Meersman, R. Data Semantics. North-Holland, 1986.

    Google Scholar 

  25. Koestler, A., The Act of Creation, Dell, 1964.

    Google Scholar 

  26. Konsynski, B.R., Database Driven Systems, University of Arizona Press, 1979.

    Google Scholar 

  27. Lewis, P.J., “Rich Picture Building in the Soft Systems Methodology”, Journal of Information Systems, Vol. 1, No. 5, 1992.

    Google Scholar 

  28. Lipowski, Z.J., Sensory and Information Inputs Overload, Comprehensive Psychiatry, Vol. 16, 3, May/June, 1975.

    Google Scholar 

  29. Martin, J., “Recommended Diagramming Standards For Analysts and Programmers: A Basis for Automation”, Prentice-Hall, Englewood Cliffs, New Jersey, 1987.

    Google Scholar 

  30. Martin, J., Strategic Data Planning Methodologies, Prentice Hall, 1989.

    Google Scholar 

  31. Miller, G., The magical number seven, plus or minus two: Some limits on our capacity for processing information, The Psychological Review, March, 1956.

    Google Scholar 

  32. Mittermier, R.T., Hsia, P. and Yeh, R.T., “Alternatives to Overcome the Communication Problem of Formal Requirements Analysis”, in Galliers, R. (ed), Information Analysis: Selected Readings, Sydney, Addison-Wesley, 1987.

    Google Scholar 

  33. Moody, D.L. and Shanks, G.G., “What Makes A Good Data Model? Evaluating the Quality of Entity Relationship Models”, Proceedings of the Thirteenth International Conference on the Entity Relationship Approach, Manchester, England, December 14–17, 1994.

    Google Scholar 

  34. Moody, D.L., “A Practical Methodology for the Representation of Large Data Models”, Proceedings of the Australian Database and Information Systems Conference, University of N.S.W., Sydney, Australia, February, 1991.

    Google Scholar 

  35. Moody, D.L., “The Seven Habits of Highly Effective Data Modellers”, Database Programming & Design, October, 1996.

    Google Scholar 

  36. Moriarty, T., “Testing from the Top”, Database Programming and Design, August, 1993b.

    Google Scholar 

  37. Moriarty, T., “Where's The Business?”, Database Programming and Design, July, 1993a.

    Google Scholar 

  38. Newell A., and Simon, H.A., Human Problem Solving, Prentice-Hall, 1972.

    Google Scholar 

  39. Page-Jones, M., A Practical Guide to Structured Systems Design, Yourdon Press, 1980.

    Google Scholar 

  40. Peckham, J. and Maryanski, F. Semantic Database Modelling: Survey, Application and Research Issues, ACM Computing Surveys, September, 1987.

    Google Scholar 

  41. Shanks, G.G., “Enterprise Data Architectures: An Empirical Study”, Monash University Working Paper, Monash University, Melbourne, Australia, August, 1996.

    Google Scholar 

  42. Simon, H.A. Sciences of the Artificial, MIT Press, 1982.

    Google Scholar 

  43. Simsion, G.C., A Structured Approach to Data Modelling, The Australian Computer Journal, August, 1989.

    Google Scholar 

  44. Simsion, G.C., Data Modelling Essentials: Analysis, Design and Innovation, International Thomson Computer Press, New York, 1994.

    Google Scholar 

  45. Swatman, P.A. and Swatman, P.M.C., “Formal Specification-An Analytic Tool For Management Information Systems”, Journal of Information Systems, April, 1992.

    Google Scholar 

  46. Teory, T.J., Yang, D. and Fry, J.P., A Logical Design Methodology for relational databases using the extended Entity Relationship Model, ACM Computing Surveys, 18, 2, 1986.

    Google Scholar 

  47. Uhr, L., Vossier, C., and Weman, J., Pattern Recognition over Distortions by Human Subjects and a Computer Model of Human Form Perception, Journal of Experimental Psychology, 63, 1962.

    Google Scholar 

  48. Wood-Harper, A.T., Antill, L., Avison, D.E., Information Systems Definition: The Multiview Approach, Blackwell Scientific Publications, Oxford, 1985.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Simsion Bowles and Associates, 1 Collins St., 3000, Melbourne, Australia

    Daniel Moody

Authors
  1. Daniel Moody
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Bernhard Thalheim

Rights and permissions

Reprints and permissions

Copyright information

© 1996 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Moody, D. (1996). Graphical Entity Relationship models: Towards a more user understandable representation of data. In: Thalheim, B. (eds) Conceptual Modeling — ER '96. ER 1996. Lecture Notes in Computer Science, vol 1157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0019926

Download citation

  • DOI: https://doi.org/10.1007/BFb0019926

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61784-6

  • Online ISBN: 978-3-540-70685-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation