IDEAS home Printed from https://ideas.repec.org/a/igg/jeis00/v12y2016i2p1-13.html
   My bibliography  Save this article

Knowledge-Based Systems for Data Modelling

Author

Listed:
  • Sabrina Šuman

    (Business Department, Polytechnic of Rijeka, Rijeka, Croatia)

  • Alen Jakupović

    (Business Department, Polytechnic of Rijeka, Rijeka, Croatia)

  • Francesca Gržinić Kuljanac

    (City of Opatija, Opatija, Croatia)

Abstract

Data modelling is a complex process that depends on the knowledge and experience of the designers who carry it out. The quality of created models has a significant impact on the quality of successive phases of information systems development. This paper, in short, reviews the data modelling process, the entity-relationship method (ERM) and actors in the data modelling process. Further, in more detail it presents systems, methods, and tools for the data modelling process and identifies problems that occur during the development phase of an information system. These problems also represent the authors' motivation for conducting research that aims to develop a knowledge-based system (KBS) in order to support the data modelling process by applying formal language theory (particularly translation) during the process of conceptual modelling. The paper describes the main identified characteristics of the authors' new KB system that are derived from the analysis of existing systems, methods, and tools for the data modelling process. This represents the focus of the research.

Suggested Citation

  • Sabrina Šuman & Alen Jakupović & Francesca Gržinić Kuljanac, 2016. "Knowledge-Based Systems for Data Modelling," International Journal of Enterprise Information Systems (IJEIS), IGI Global, vol. 12(2), pages 1-13, April.
  • Handle: RePEc:igg:jeis00:v:12:y:2016:i:2:p:1-13
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJEIS.2016040101
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jeis00:v:12:y:2016:i:2:p:1-13. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.