IDEAS home Printed from https://ideas.repec.org/a/prg/jnlaip/v2022y2022i3id193p293-308.html
   My bibliography  Save this article

A Novel Automatic Relational Database Normalization Method

Author

Listed:
  • Emre Akadal
  • Mehmet Hakan Satman

Abstract

The increase in data diversity and the fact that database design is a difficult process make it practically impossible to design a unique database schema for all datasets encountered. In this paper, we introduce a fully automatic genetic algorithm-based relational database normalization method for revealing the right database schema using a raw dataset and without the need for any prior knowledge. For measuring the performance of the algorithm, we perform a simulation study using 250 datasets produced using 50 well-known databases. A total of 2500 simulations are carried out, ten times for each of five denormalized variations of all database designs containing different synthetic contents. The results of the simulation study show that the proposed algorithm discovers exactly 72% of the unknown database schemas. The performance can be improved by fine-tuning the optimization parameters. The results of the simulation study also show that the devised algorithm can be used in many datasets to reveal structs of databases when only a raw dataset is available at hand.

Suggested Citation

  • Emre Akadal & Mehmet Hakan Satman, 2022. "A Novel Automatic Relational Database Normalization Method," Acta Informatica Pragensia, Prague University of Economics and Business, vol. 2022(3), pages 293-308.
    • Handle: RePEc:prg:jnlaip:v:2022:y:2022:i:3:id:193:p:293-308
    DOI: 10.18267/j.aip.193
    as

    Download full text from publisher

    File URL: http://aip.vse.cz/doi/10.18267/j.aip.193.html
    Download Restriction: free of charge
    File URL: http://aip.vse.cz/doi/10.18267/j.aip.193.pdf
    Download Restriction: free of charge
    File URL: https://libkey.io/10.18267/j.aip.193?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:prg:jnlaip:v:2022:y:2022:i:3:id:193:p:293-308. 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: Stanislav Vojir (email available below). General contact details of provider: https://edirc.repec.org/data/uevsecz.html .

    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.