Advertisement

Knowledge and Information Systems

, Volume 6, Issue 3, pp 315–344 | Cite as

Contextual Partitioning for Comprehension of OODB Schemas

  • Huanying GuEmail author
  • Yehoshua Perl
  • Michael Halper
  • James Geller
  • Erich J. Neuhold
Article
  • 37 Downloads

Abstract.

Object-oriented databases (OODBs) have been utilized for complex modeling tasks within a variety of application domains. The OODB schema, typically expressed in a graphical notation, can serve as a useful presentation tool for the information contained in the underlying OODB. However, such a schema can be a large, complex network of classes and relationships. This may greatly hinder its effectiveness in helping users gain an understanding of the OODB’s contents and data organization. To facilitate this orientation process, a theoretical framework is presented that guides the refinement\/ of an existing schema’s subclass-of\/ relationship hierarchy – the backbone of any OODB. The framework sets forth three rules which, when satisfied, lead to the establishment of a collection of contexts, each of which exhibits an internal subclass-of\/ tree structure. A formal proof of this result is presented. An algorithmic methodology, involving a human–computer interaction, describes how the approach can be applied to a given OODB schema. An application of the methodology to an example OODB schema is included.

Keywords

Comprehension Context Object-oriented database modeling Object-oriented database schema Schema partitioning Subclass hierarchy User orientation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag London Limited 2004

Authors and Affiliations

  • Huanying Gu
    • 1
    Email author
  • Yehoshua Perl
    • 2
  • Michael Halper
    • 3
  • James Geller
    • 2
  • Erich J. Neuhold
    • 4
  1. 1.Department of Health InformaticsUMDNJNJUSA
  2. 2.Computer Science DepartmentNJITNJUSA
  3. 3.Department of Mathematics and Computer ScienceKean UniversityNJUSA
  4. 4.Fraunhofer IPSIDarmstadtGermany

Personalised recommendations