A Methodology for Clustering Entity Relationship Models — A Human Information Processing Approach

  • Daniel L. Moody
  • Andrew Flitman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1728)


This paper defines a method for decomposing a large data model into a hierarchy of models of manageable size. The purpose of this is to (a) improve user understanding and (b) simplify documentation and maintenance. Firstly, a set of principles is defined which prescribe the characteristics of a “good” decomposition. These principles may be used to evaluate the quality of a decomposition and to choose between alternatives. Based on these principles, a manual procedure is described which can be used by a human expert to produce a relatively optimal clustering. Finally, a genetic algorithm is described which automatically finds an optimal decomposition. A key differentiating factor between this and previous approaches is that it is soundly based on principles of human information processing—this ensures that data models are clustered in a way that can be most efficiently processed by the human mind.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    AKOKA, J. and COMYN-WATTIAU, I. (1996). Entity Relationship and Object Oriented Model Automatic Clustering., Data and Knowledge Engineering, 20.Google Scholar
  2. 2.
    ALEXANDER, C. (1968): Notes on the Synthesis of Form, Harvard University Press, Boston.Google Scholar
  3. 3.
    ANDERSON, J.R. and PIROLLI, P.L. (1984):. Spread of Activation., Journal of Experimental Psychology: Learning, Memory and Cognition, 10, 4.Google Scholar
  4. 4.
    BADDELEY, A. (1994):. The Magical Number Seven: Still Magic After All These Years?., Psychological Review, 101, 2.Google Scholar
  5. 5.
    BATINI, C., CERI, S. and NAVATHE, S.B. (1992) Conceptual Database Design: An Entity Relationship Approach, Benjamin Cummings, Redwood City, California.zbMATHGoogle Scholar
  6. 6.
    COLLINS, A.M. and QUILLIAN, M.R. (1969):. Retrieval Time from Semantic Memory., Journal of Verbal Learning and Verbal Behaviour, 8.Google Scholar
  7. 7.
    COLLINS, A.M. and QUILLIAN, M.R. (1972):. How to Make a Language User., in Organisation and Memory, E. Tulving and M. Donaldson (ed.s), Academic Press, New York.Google Scholar
  8. 8.
    DAVIS, G.B. and OLSEN, M.H. (1985): Management Information Systems: Conceptual Foundations, Structure and Development, McGraw-Hill.Google Scholar
  9. 9.
    DE MARCO, T. (1978): Structured Analysis and System Specification, Yourdon Press, 1978.Google Scholar
  10. 10.
    EYSENCK, M.W. AND KEANE, M.T. (1992): Cognitive Psychology: A Student.s Handbook, Lawrence Erlbaum Associates, Hove and London.Google Scholar
  11. 11.
    FELDMAN, P. and MILLER, D., (1986): Entity Model Clustering: Structuring a Data Model by Abstraction, The Computer Journal, Vol. 29, No. 4.Google Scholar
  12. 12.
    FLOOD, R.L. and CARSON, E.R. (1993): Dealing With Complexity: An Introduction to the Theory and Application of Systems Science, Plenum Press.Google Scholar
  13. 13.
    FRANCALANCI, C. and PERNICI, B. (1994):. Abstraction Levels for Entity Relationship Schemas., in P. LOUCOPOULOS (ed.) Proceedings of the Thirteenth International Conference on the Entity Relationship Approach, Manchester, December 14–17.Google Scholar
  14. 14.
    GOLDBERG, D. (1989):. Genetic Algorithms in Search, Optimization, and Machine Learning., Addison Wesley, p15–21.Google Scholar
  15. 15.
    IVARI, J. (1986): Dimensions Of Information Systems Design: A Framework For A Long Range Research Program. Information Systems, June, 39–42.Google Scholar
  16. 16.
    KLIR, G.J. (1985): Architecture of Systems Problem Solving, Plenum Press, New York.zbMATHGoogle Scholar
  17. 17.
    MARTIN, J. (1983): Strategic Data Planning Methodologies, Prentice-Hall.Google Scholar
  18. 18.
    MILLER, G. (1956): The Magical Number Seven, Plus Or Minus Two: Some Limits On Our Capacity For Processing Information, The Psychological Review, March.Google Scholar
  19. 19.
    MOODY, D.L. and FLITMAN, A. (1999):. Principles. Metrics and an Algorithm for Clustering Entity Relationship Models., Department of Information Systems Working Paper, University of Melbourne, Parkville, Victoria, Australia.Google Scholar
  20. 20.
    MOODY, D.L. and WALSH, P.A., (1999). Measuring the Value of Information: An Asset Valuation Approach., Proceedings of the Seventh European Conference on Information Systems (ECIS.99), Copenhagen, Denmark, June 23-25.Google Scholar
  21. 21.
    MOODY, D.L.,. A Multi-Level Architecture for Representing Enterprise Data Models., Proceedings of the Sixteenth International Conference on Conceptual Modelling (ER’97), Los Angeles, November 1–3, 1997.Google Scholar
  22. 22.
    PIPPENGER, N. (1978): Complexity Theory, Scientific American, 238(6): 1–15.CrossRefGoogle Scholar
  23. 23.
    O’REILLY, C.A. (1980): Individuals and Information Overload in Organisations: Is More Necessarily Better?, Academy of Management Journal, Vol 23, No. 4.Google Scholar
  24. 24.
    SCHAFFER D., CARUANA R., ESHELMAN L., RAJARSHI D. (1989):. A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization., Proceedings Of The 3rd International Conference on Genetic Algorithms, 1989, p.51–61, Morgan KaufmannGoogle Scholar
  25. 25.
    SIMON, H.A. (1982): Sciences of the Artificial, MIT Press.Google Scholar
  26. 26.
    SMITH, J.M. and SMITH, D.C.P. (1977): Database Abstractions: Aggregation and Generalization, ACM Transactions on Database Systems, Vol. 2 No. 2.Google Scholar
  27. 27.
    TEORY, T.J., WEI, G., BOLTON, D.L., and KOENIG, J.A. (1989): ER Model Clustering as an Aid for User Communication and Documentation in Database Design, Communications of the ACM, August.Google Scholar
  28. 28.
    UHR, L., VOSSIER, C., and WEMAN, J. (1962): Pattern Recognition over Distortions by Human Subjects and a Computer Model of Human Form Perception, Journal of Experimental Psychology, 63.Google Scholar
  29. 29.
    WAND, Y. and WEBER, R.A. (1990): A Model for Systems Decomposition, in J.I. De-Gross, M. Alavi, and H. Oppelland (Ed.s), Proceedings of the Eleventh International Conference on Information Systems, Copenhagen, Denmark, December.Google Scholar
  30. 30.
    WEBER, R.A. (1997): Ontological Foundations of Information Systems, Coopers and Lybrand Accounting Research Methodology Monograph No. 4, Coopers and Lybrand Australia, Melbourne, Australia.Google Scholar
  31. 31.
    YOURDON, E. and CONSTANTINE, L.L. (1979):. Structured Design: Fundamentals of a Discipline of Computer Program and Systems Design., Prentice-Hall, Englewood Cliffs, NJ.zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Daniel L. Moody
    • 1
  • Andrew Flitman
    • 2
  1. 1.Department of Information SystemsUniversity of MelbourneMelbourne
  2. 2.School of Business SystemsMonash UniversityAustralia

Personalised recommendations