Advertisement

Incompleteness in Conceptual Data Modelling

  • Peter Thanisch
  • Tapio Niemi
  • Jyrki Nummenmaa
  • Zheying Zhang
  • Marko Niinimäki
  • Pertti Saariluoma
Part of the Communications in Computer and Information Science book series (CCIS, volume 403)

Abstract

Although conceptual data modelers can ”get creative” when designing entities and relationships to meet business requirements, they are highly constrained by the business rules which determine the details of how the entities and relationships combine. Typically, there is a delay in realising which business rules might be relevant and a further delay in obtaining an authoritative statement of these rules. We identify circumstances under which viable database designs can be constructed from conceptual data models which are incomplete in the sense that they lack this “infrastructural” detail normally obtained from the business rules. As such detail becomes available, our approach allows the conceptual model to be incrementally refined so that each refinements can be associated with standard database refactorings, minimising the impact on database operations. Our incremental approach facilitates the implementation of the database earlier in the development cycle.

Keywords

Conceptual data modeling entity-relationship database refactoring 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ambler, S., Sadalage, P.J.: Refactoring Databases: Evolutionary Database Design. Addison Wesley (2006)Google Scholar
  2. 2.
    Do Nascimento Fidalgo, R., De Souza, E.M., España, S., De Castro, J.B., Pastor, O.: EERMM: A Metamodel for the Enhanced Entity-Relationship Model. In: Atzeni, P., Cheung, D., Ram, S. (eds.) ER 2012 Main Conference 2012. LNCS, vol. 7532, pp. 515–524. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Galindo, J., Urrutia, A., Carrasco, R.A., Piattini, M.: Relaxing constraints in enhanced entity-relationship models using fuzzy quantifiers. IEEE T. Fuzzy Systems 12, 780–796 (2004)CrossRefGoogle Scholar
  4. 4.
    Golfarelli, M., Rizzi, S., Turricchia, E.: Sprint planning optimization in agile data warehouse design. In: Cuzzocrea, A., Dayal, U. (eds.) DaWaK 2012. LNCS, vol. 7448, pp. 30–41. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  5. 5.
    Larman, C., Basili, V.R.: Iterative and Incremental Development A Brief History. Computer 36, 47–56 (2003)CrossRefGoogle Scholar
  6. 6.
    Ma, Z.M., Yan, L.: A Literature overview of fuzzy conceptual data modeling. Journal of Information Science And Engineering 26, 427–441 (2010)Google Scholar
  7. 7.
    Salay, R., Chechik, M., Horkoff, J.: Managing Requirements Uncertainty with Partial Models. In: Proc. of Requirements Engineering, pp. 1–10 (2012)Google Scholar
  8. 8.
    Teorey, T., Lightstone, S., Nadeau, T.: Database Modeling and Design: Logical Design, 4th edn. Morgan Kaufmann, San Francisco (2006)Google Scholar
  9. 9.
    Thalheim, B.: The science and art of conceptual modelling. In: Hameurlain, A., Küng, J., Wagner, R., Liddle, S.W., Schewe, K.-D., Zhou, X. (eds.) Transactions on Large-Scale Data- and Knowledge-Centered Systems VI. LNCS, vol. 7600, pp. 76–105. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Thalheim, B., Wang, Q.: Towards a theory of refinement for data migration. In: Jeusfeld, M., Delcambre, L., Ling, T.-W. (eds.) ER 2011. LNCS, vol. 6998, pp. 318–331. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Peter Thanisch
    • 1
  • Tapio Niemi
    • 2
  • Jyrki Nummenmaa
    • 1
  • Zheying Zhang
    • 1
  • Marko Niinimäki
    • 2
  • Pertti Saariluoma
    • 3
  1. 1.School of Information SciencesUniversity of TampereTampereFinland
  2. 2.Helsinki Institute of PhysicsUniversity of HelsinkiFinland
  3. 3.University of JyväskyläFinland

Personalised recommendations