On Computing the Importance of Entity Types in Large Conceptual Schemas

  • Antonio Villegas
  • Antoni Olivé
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5833)


The visualization and the understanding of large conceptual schemas require the use of specific methods. These methods generate clustered, summarized or focused schemas that are easier to visualize and to understand. All of these methods require computing the importance of each entity type in the schema. In principle, the totality of knowledge defined in the schema could be relevant for the computation of that importance but, up to now, only a small part of that knowledge has been taken into account. In this paper, we extend six existing methods for computing the importance of entity types by taking into account all the relevant knowledge defined in the structural and behavioural parts of the schema. We experimentally evaluate the original and the extended versions of those methods with two large real-world schemas. We present the two main conclusions we have drawn from the experiments.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Olivé, A., Cabot, J.: A research agenda for conceptual schema-centric development. In: Krogstie, J., Opdahl, A.L., Brinkkemper, S. (eds.) Conceptual Modelling in Information Systems Engineering, pp. 319–334. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Lindland, O.I., Sindre, G., Sølvberg, A.: Understanding quality in conceptual modeling. IEEE Software 11(2), 42–49 (1994)CrossRefGoogle Scholar
  3. 3.
    Castano, S., Antonellis, V.D., Fugini, M.G., Pernici, B.: Conceptual schema analysis: Techniques and applications. ACM Trans. Database Syst. 23(3), 286–332 (1998)CrossRefGoogle Scholar
  4. 4.
    Moody, D.L., Flitman, A.: A Methodology for Clustering Entity Relationship Models – A Human Information Processing Approach. In: Akoka, J., Bouzeghoub, M., Comyn-Wattiau, I., Métais, E. (eds.) ER 1999. LNCS, vol. 1728, pp. 114–130. Springer, Heidelberg (1999)Google Scholar
  5. 5.
    Tzitzikas, Y., Hainaut, J.L.: How to tame a very large ER diagram (using link analysis and force-directed drawing algorithms). In: Delcambre, L.M.L., Kop, C., Mayr, H.C., Mylopoulos, J., Pastor, Ó. (eds.) ER 2005. LNCS, vol. 3716, pp. 144–159. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Tzitzikas, Y., Kotzinos, D., Theoharis, Y.: On ranking rdf schema elements (and its application in visualization). J. UCS 13(12), 1854–1880 (2007)Google Scholar
  7. 7.
    Yu, C., Jagadish, H.V.: Schema summarization. In: Dayal, U., Whang, K.Y., Lomet, D.B., Alonso, G., Lohman, G.M., Kersten, M.L., Cha, S.K., Kim, Y.K. (eds.) VLDB, pp. 319–330. ACM, New York (2006)Google Scholar
  8. 8.
    Olivé, A.: Conceptual Modeling of Information Systems. Springer, Heidelberg (2007)MATHGoogle Scholar
  9. 9.
    Tort, A., Olivé, A.: The osCommerce Conceptual Schema. Universitat Politècnica de Catalunya (2007), http://guifre.lsi.upc.edu/OSCommerce.pdf
  10. 10.
    Object Management Group (OMG): Unified Modeling Language (UML) Superstructure Specification, version 2.2 (February 2009)Google Scholar
  11. 11.
    Object Management Group (OMG): Object Constraint Language Specification (OCL), version 2.0 (May 2006)Google Scholar
  12. 12.
    Baroni, A.L.: Formal definition of object-oriented design metrics. Master’s thesis, Vrije Universiteit Brussel (2002)Google Scholar
  13. 13.
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. In: Computer Networks and ISDN Systems, pp. 107–117. Elsevier Science Publishers B. V., Amsterdam (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Antonio Villegas
    • 1
  • Antoni Olivé
    • 1
  1. 1.Dept. de Llenguatges i Sistemes InformàticsUniversitat Politècnica de Catalunya 

Personalised recommendations