The Guidelines of Modeling – An Approach to Enhance the Quality in Information Models

  • Reinhard Schuette
  • Thomas Rotthowe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1507)


Although the necessity of building models seems obvious, it is astonishing that only few works deal with the fundamental problems of modeling – how models need to be understood from an epistemological point of view (e.g.: how does a modeler come to a model; why and in which ways do models built by different designers differ from another). The paper bases on the assumption that the subjective position of the modeler is the characterizing issue for the result of the modeling process – and that this subjectivity needs to be managed. We derive from this assumption the need for a sound structural framework in order to deal with the subjectivism of building models. With the Guidelines of Modeling (GoM) we present a framework of principles that improve the quality of information models by reducing the subjectivism in the information modeling process. The quality of modeling is supported by recommendations for an efficient, comprehensive, and correct design of information models. In the first part of the paper the guidelines are derived from the specific problems that stem from the subjective process of the system design. The Guidelines of Modeling contain six principles to ameliorate the quality of information modeling which are described in detail. Subsequently, the basic guidelines are placed in a structural framework, the GoM-Architecture, which consists of two dimensions: 1st the range of model-use (reference models for a class of organizations or an industry, and company-specific models) and 2nd the degree of precision or concretion (general, system views, and description language).


Unify Modeling Language Model User Information Modeling Information Object Layout Design 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Albert, H.: Traktat ueber rationale Praxis, Tuebingen (1978)Google Scholar
  2. 2.
    Albert, H.: Kritische Vernunft und menschliche Praxis, Stuttgart (1977)Google Scholar
  3. 3.
    Batini, C., Ceri, S., Navathe, S.B.: Conceptual Database Design. An Entity-Relationship Approach, Redwood City et al. (1992)Google Scholar
  4. 4.
    Batini, C., Furlani, L., Nardelli, E.: What is a good diagramm? A pragmatic approach. In: Proceedings of the 4th International Conference on the Entity-Relationship Approach: The Use of ER Concepts in Knowledge Representation, Berlin et al., pp. 312–319 (1985)Google Scholar
  5. 5.
    Batini, C., Nardelli, E., Tamassia, R.: A Layout Algorithm for Data Flow Diagrams. IEEE Transactions on Software Engineering 12(4), 538–546 (1986)Google Scholar
  6. 6.
    Batini, C., Talamo, M., Tamassia, R.: Computer Aided Layout of Entity Relationship Diagrams. The Journal of Systems and Software 4, 163–173 (1984)CrossRefGoogle Scholar
  7. 7.
    Becker, J.: The Architecture of retail information systems. In: Becker, J., Grob, H.L., Müller-Funk, U., Vossen, G. (eds.) Working papers of the Department of Information Systems, Muenster, vol. 46 (1996)Google Scholar
  8. 8.
    Becker, J., Ehlers, L., Schuette, R.: Grundsaetze ordnungsmaessiger Modellierung. Konzeption, Vorgehensmodell, informationstechnische Realisierung, Nutzen. In: Grote, U., Wolf, G. (eds.) Statusseminar des BMBF Softwaretechnologie. Projekttraeger Informationstechnik des BMBF bei der DLR e.V. im Auftrag des BMBF, Berlin, Maerz 23-24 (1998)Google Scholar
  9. 9.
    Bretzke, W.-R.: Der Problembezug von Entscheidungsmodellen, Tuebingen (1980)Google Scholar
  10. 10.
    Becker, J., Rosemann, M., Schuette, R.: Grundsaetze ordnungsmaessiger Modellierung. Wirtschaftsinformatik 37(5), 435–445 (1995)Google Scholar
  11. 11.
    Becker, J., Rosemann, M., Schuette, R., Rotthowe, T.: A Framework for Efficient Information Modeling - Guidelines for Retail Enterprises. In: Paper accepted at INFORMS Conference on Information Systems and Technology, Montreal (April 1998)Google Scholar
  12. 12.
    Boehm, B.W.: Software Engineering Economics, Englewood Cliffs (1981)Google Scholar
  13. 13.
    Bostrom, R.P.: Successful Application of Communication Techniques to Improve the Systems Development Process. Information & Management 16, 279–295 (1989)CrossRefGoogle Scholar
  14. 14.
    Bunge, M.: Scientific Research I. The Search for Systems, Berlin et al. (1967)Google Scholar
  15. 15.
    Daneva, M., Heib, R., Scheer, A.-W.: Benchmarking Business Process Models. In: Scheer, A.-W. (ed.) Working papers of the Institut fuer Wirtschaftsinformatik. Heft 136, Saarbruecken (1996)Google Scholar
  16. 16.
    Elmasri, R., Navathe, S.B.: Fundamentals of Database Systems, 2nd edn., Redwood City et al. (1994)Google Scholar
  17. 17.
    Hars, A.: Referenzdatenmodelle. Grundlagen effizienter Datenmodellierung, Wiesbaden (1994)Google Scholar
  18. 18.
    Harwryszkiewycz, T.H.: Introduction to systems analysis and design, New York et al. (1991)Google Scholar
  19. 19.
    Hesse, W., Barkow, G., Braun, H.v., Kittlaus, H.-B., Scheschonk, G.: Terminologie der Softwaretechnik. Ein Begriffssystem fuer die Analyse und Modellierung von Anwendungssystemen. Teil 2. Informatik Spektrum 17(2), 96–105 (1994)Google Scholar
  20. 20.
    Hudson, D.L.: Practical Model Management Using CASE Tools. QED Publishing Group Boston, London (1993)Google Scholar
  21. 21.
    Keller, G., Nuettgens, M., Scheer, A.-W.: Semantische Prozessmodellierung auf der Basis Ereignisgesteuerter Prozessketten (EPK). In: Scheer, A.-W. (ed.) Working papers of the Institut fuer Wirtschaftsinformatik, Saarbruecken, vol. 89 (1992)Google Scholar
  22. 22.
    Krogstie, J.: Conceptual Modeling for Computerized Information Systems Support in Organizations. PhD Thesis, Trondheim (1995)Google Scholar
  23. 23.
    Krogstie, J., Lindland, O.I., Sindre, G.: Defining Quality Aspects for Conceptual Models. In: Proceedings of the International Conference on Information System Concepts (ISCO3)Towards a Consolidation of Views, Marburg (1995) PreprintGoogle Scholar
  24. 24.
    Krogstie, J., Lindland, O.I., Sindre, G.: Towards a Deeper Understanding of Quality in Requirements Engineering. In: Iivari, J., Lyytinen, K., Rossi, M. (eds.) CAiSE 1995, Berlin, pp. 82–95 (1995)Google Scholar
  25. 25.
    Krogstie, J., SØlvberg, A.: A Classification of Methodological Frameworks for Computerized Information Systems in Organizations. In: Brinkkemper, S., Lyytinen, K., Welke, R.J. (eds.) Method Engineering, London, et al., pp. 278–295 (1996)Google Scholar
  26. 26.
    Lindland, O.I., Sindre, G., SØlvberg, A.: Understanding Quality in Conceptual Modeling. IEEE Software 11(2), 42–49 (1994)CrossRefGoogle Scholar
  27. 27.
    Maier, R.: Qualitaet von Datenmodellen, Wiesbaden (1996)Google Scholar
  28. 28.
    Moody, D.: Graphical Entity Relationship Models. Towards a More User Understandable Representation of Data. In: Thalheim, B. (ed.) ER 1996. LNCS, vol. 1157, pp. 227–244. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  29. 29.
    McMenamim, S.M., Palmer, J.F.: Essential Systems Analysis, New York (1984)Google Scholar
  30. 30.
    Moody, D.L., Shanks, S.: What Makes a Good Data Model? Evaluating the Quality of Entity Relationship Models. In: Loucopoulos, P. (ed.) ER 1994. LNCS, vol. 881, pp. 94–111. Springer, Heidelberg (1994)Google Scholar
  31. 31.
    Oliver, I., Langford, H.: Myths of Demons and Users. Evidence and Analysis of Negative Perceptions of Users. In: Galliers, R. (ed.) Information Analysis. Selected Readings, pp. 113–123. Addison-Wessly Publishing, Sydney et al. (1987)Google Scholar
  32. 32.
    Ortner, E., Schienmann, B.: Normative Language Approach. A Framework for Understanding. In: Thalheim, B. (ed.) ER 1996. LNCS, vol. 1157, pp. 261–276. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  33. 33.
    Pohl, K.: Process-Centered Requirements Engineering. Taunton, Somerset (1996)Google Scholar
  34. 34.
    Pohl, K.: The Three Dimension of Requirements Engineering. In: Rolland, C., Cauvet, C., Bodart, F. (eds.) CAiSE 1993. LNCS, vol. 685, pp. 275–292. Springer, Heidelberg (1993)Google Scholar
  35. 35.
    Popper, K.R.: Objective Knowledge, 4th edn., Oxford (1984)Google Scholar
  36. 36.
    Rauh, O.: Guetekriterien fuer die semantische Datenmodellierung. HMD 28(158), 91–110 (1991)Google Scholar
  37. 37.
    Rescher, N.: Pluralism. Against the Demand for Consensus. Clarendon Press, Oxford (1993)Google Scholar
  38. 38.
    Rescher, N.: Objectivity. The Obligations of Impersonal Reason. Notre Dame, London (1997)Google Scholar
  39. 39.
    SAP: R/3 System, July 14 (1998),
  40. 40.
    Scheer, A.-W.: Business Process Reengineering. In: Scheer, A.-W. (ed.) Reference Models for Industrial Enterprises, 2nd edn., Berlin et al (1994)Google Scholar
  41. 41.
    Schuette, R.: Handelsinformationssysteme, Muenster, March 14 (1994) (Unpublished presentation)Google Scholar
  42. 42.
    Schuette, R.: Grundsaetze ordnungsmaessiger Referenzmodellierung. Dissertation,Universitaet Muenster. Muenster ( 1997), to be published in Gabler Verlag, Reihe neue betriebswirtschaftliche forschung (nbf), Wiesbaden (1997)Google Scholar
  43. 43.
    Schuette, R.: Grundsaetze ordnungsmäßiger Modellierung. Reformulierung eines Ansatzes zur Informationsmodellqualität. Paper presented at the research colloquium at the Institute for Computer Sciences of the University of Koblenz. Koblenz, December 04 (1997), (
  44. 44.
    Shepherd, G.M.: Neurobiology, 2nd edn. Oxford University Press, Oxford (1989)Google Scholar
  45. 45.
    Seltveit, A. H.: Complexity Reduction in Information Systems Modeling. PhD thesis University of Trondheim. Trondheim (1994)Google Scholar
  46. 46.
    Tamassia, D., Di Battisti, G., Batini, C.: Automatic graph drawing and readibility of diagrams. IEEE Transactions on Systems, Man and Cybernetics 18(1), 61–79 (1988)CrossRefGoogle Scholar
  47. 47.
    Ulrich, H.: Die Unternehmung als produktives und soziales System, Bern (1968)Google Scholar
  48. 48.
    Vollmer, G.: Evolutionaere Erkenntnistheorie, 6th edn., Stuttgart (1994)Google Scholar
  49. 49.
    Zamperoni, A., Loehr-Richter, P.: Enhancing the Quality of Conceptual Database Specifications through Validation. In: Elmasri, R.A., Kouramajian, V., Thalheim, B. (eds.) ER 1993. LNCS, vol. 823. Springer, Heidelberg (1994)Google Scholar
  50. 50.
    Zelewski, S.: Petrinetzbasierte Modellierung komplexer Produktionssysteme. Band 2. Bezugsrahmen. Working paper no. 6 of Institut für Produktionswirtschaft und Industrielle Informationswirtschaft, Leipzig (1995)Google Scholar
  51. 51.
    Zentes, J.: Die Optimalkomplexion von Entscheidungsmodellen. Ein Beitrag zur betriebswirtschaftlichen Meta-Entscheidungstheorie, Köln et al. (1976)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Reinhard Schuette
    • 1
  • Thomas Rotthowe
    • 2
  1. 1.Department of Business Administration, especially Production and Information ManagementUniversity of EssenEssenGermany
  2. 2.Department of Information SystemsUniversity of MünsterMuensterGermany

Personalised recommendations