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Postponing Schema Definition: Low Instance-to-Entity Ratio (LItER) Modelling

  • John F. Roddick
  • Aaron Ceglar
  • Denise de Vries
  • Somluck La-Ongsri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4512)

Abstract

There are four classes of information system that are not well served by current modelling techniques. First, there are systems for which the number of instances for each entity is relatively low resulting in data definition taking a disproportionate amount of effort. Second, there are systems where the storage of data and the retrieval of information must take priority over the full definition of a schema describing that data. Third, there are those that undergo regular structural change and are thus subject to information loss as a result of changes to the schema’s information capacity. Finally, there are those systems where the structure of the information is only partially known or for which there are multiple, perhaps contradictory, competing hypotheses as to the underlying structure.

This paper presents the Low Instance-to-Entity Ratio (LItER) Model, which attempts to circumvent some of the problems encountered by these types of application. The two-part LItER modelling process possesses an overarching architecture which provides hypothesis, knowledge base and ontology support together with a common conceptual schema. This allows data to be stored immediately and for a more refined conceptual schema to be developed later. It also facilitates later translation to EER, ORM and UML models and the use of (a form of) SQL. Moreover, an additional benefit of the model is that it provides a partial solution to a number of outstanding issues in current conceptual modelling systems.

Keywords

Query Language Conceptual Schema Graph Mining Data Dictionary Common Data Model 
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.

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References

  1. 1.
    Chen, P.P.S.: The entity-relationship model - toward a unified view of data. ACM Transactions on Database Systems 1, 9–36 (1976)CrossRefGoogle Scholar
  2. 2.
    Thalheim, B.: Entity-Relationship Modeling: Foundations of Database Technology. Springer, Berlin (2000)CrossRefzbMATHGoogle Scholar
  3. 3.
    Halpin, T.: Object-role modeling (ORM/NIAM). In: Bernus, P., Mertins, K., Schmidt, G. (eds.) Handbook on Architectures of Information Systems, pp. 81–101. Springer, Berlin (1998)Google Scholar
  4. 4.
    Verheijen, G., van Bekkum, J.: NIAM: an information analysis method. In: IFIP WG8.I Working conf. Information Systems Design Methodologies: a comparative review, North Holland Publishing, Netherlands (1982)Google Scholar
  5. 5.
    Booch, G., Jacobson, I., Rumbaugh, J.: Unified modelling language user guide, 2nd edn. Addison Wesley Professional, Reading (2005)Google Scholar
  6. 6.
    Sommerville, I.: Software Engineering, 8th edn. Addison-Wesley, Boston, MA, USA (2006)zbMATHGoogle Scholar
  7. 7.
    Chen, P.P.S.: Suggested research directions for a new frontier - active conceptual modeling. In: Embley, D.W., Olivé, A., Ram, S. (eds.) ER 2006. LNCS, vol. 4215, pp. 1–4. Springer, Heidelberg (2006)Google Scholar
  8. 8.
    Roddick, J.F., Ceglar, A., de Vries, D.: Towards active conceptual modelling for sudden events. In: Grundy, J., Hartmann, S., Laender, A., Maciaszek, L., Roddick, J. (eds.) 26th International Conference on Conceptual Modeling (ER 2007) (Posters). CRPIT, Auckland, New Zealand, vol. 83, pp. 203–208. ACS (2007)Google Scholar
  9. 9.
    Roddick, J.F., de Vries, D.: Reduce, reuse, recycle: Practical approaches to schema integration, evolution and versioning. invited keynote address. In: Grandi, F. (ed.) ECDM 2006. LNCS, vol. 4231, pp. 209–216. Springer, Heidelberg (2006)Google Scholar
  10. 10.
    Shoshani, A., Wong, H.K.T.: Statistical and scientific database issues. IEEE Transactions on Software Engineering 11, 1040–1047 (1985)CrossRefGoogle Scholar
  11. 11.
    Chen, H., Zeng, D., Atabakhsh, H., Wyzga, W., Schroeder, J.: Coplink: managing law enforcement data and knowledge. Communications of the ACM 46, 28–34 (2003)CrossRefGoogle Scholar
  12. 12.
    Roddick, J.F., Craske, N.G., Richards, T.J.: Handling discovered structure in database systems. IEEE Transactions on Knowledge and Data Engineering 8, 227–240 (1996)CrossRefGoogle Scholar
  13. 13.
    de Vries, D., Rice, S., Roddick, J.F.: In support of mesodata in database management systems. In: Galindo, F., Takizawa, M., Traunmüller, R. (eds.) DEXA 2004. LNCS, vol. 3180, pp. 663–674. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  14. 14.
    de Vries, D.: Mesodata: Engineering Domains for Attribute Evolution and Data Integration. PhD thesis, Flinders University (2006)Google Scholar
  15. 15.
    Chakrabarti, D., Faloutsos, C.: Graph mining: Laws, generators, and algorithms. ACM Computing Surveys 38 (2006)Google Scholar
  16. 16.
    Ceglar, A., Roddick, J.F.: Association mining. ACM Computing Surveys 38 (2006)Google Scholar
  17. 17.
    Elmasri, R., Weeldreyer, J.A., Hevner, A.R.: The category concept: an extension to the entity-relationship model. Data and Knowledge Engineering 1, 75–116 (1985)CrossRefGoogle Scholar
  18. 18.
    Wand, Y., Storey, V.C., Weber, R.: An ontological analysis of the relationship construct in conceptual modeling. ACM Transactions on Database Systems 24, 494–518 (1999)CrossRefGoogle Scholar
  19. 19.
    Spencer, J.: The Strange Logic of Random Graphs. Springer, Heidelberg (2001)CrossRefzbMATHGoogle Scholar
  20. 20.
    Reiter, R.: On closed world databases. In: Gallaire, H., Minker, J. (eds.) Logic and Databases, pp. 55–76. Plenum Press, New York (1978) reprinted In: Mylopoulos, J., Brodie, M.L. (eds.) Artificial Intelligence and Databases, pp. 248–258. Morgan Kaufmann Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • John F. Roddick
    • 1
  • Aaron Ceglar
    • 1
  • Denise de Vries
    • 1
  • Somluck La-Ongsri
    • 1
  1. 1.School of Informatics and Engineering, Flinders University, P.O. Box 2100, Adelaide, South Australia 5001Australia

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