IDEAS home Printed from https://ideas.repec.org/a/vrs/brcebe/v3y2017i1p301-308n39.html
   My bibliography  Save this article

Organizational knowledge management with Big Data. The foundation of using artificial intelligence

Author

Listed:
  • Paschek Daniel
  • Mocan Anca
  • Dufour Corina-Monica
  • Draghici Anca

    (Politehnica University Timisoara - Management Faculty, 300191 Timisoara, Romania)

Abstract

In the following paper the relevance of Knowledge Management (KM) as a foundation of Artificial Intelligence (AI) systems will be analyzed. The purpose of the work is the presentation of mandatory framework conditions for using AI with a special view on knowledge management for Big Data. Therefore the mandatory definitions of the core components will be described theoretically supported by practical examples. Based on literature, there will be done research and presentation of existing applications the relation between the knowledge management in the organization and big data as core component. To identify the relevant topics of using Big Data for knowledge management an analysis will be held up with digital companies. In addition, the main advantages and disadvantages will be depicted. The finding of the paper will be a recommendation of the developed Artificial Intelligence Knowledge Model for using Knowledge Management and Big Data for Artificial Intelligence decisions within the company.

Suggested Citation

  • Paschek Daniel & Mocan Anca & Dufour Corina-Monica & Draghici Anca, 2017. "Organizational knowledge management with Big Data. The foundation of using artificial intelligence," Balkan Region Conference on Engineering and Business Education, Sciendo, vol. 3(1), pages 301-308, December.
  • Handle: RePEc:vrs:brcebe:v:3:y:2017:i:1:p:301-308:n:39
    DOI: 10.1515/cplbu-2017-0039
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/cplbu-2017-0039
    Download Restriction: no

    File URL: https://libkey.io/10.1515/cplbu-2017-0039?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
    ---><---

    Other versions of this item:

    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:vrs:brcebe:v:3:y:2017:i:1:p:301-308:n:39. 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.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.