IDEAS home Printed from https://ideas.repec.org/a/ids/ijidsc/v12y2020i1p1-35.html
   My bibliography  Save this article

A new approach agent-based for distributing association rules by business to improve decision process in ERP systems

Author

Listed:
  • Merouane Zoubeidi
  • Okba Kazar
  • Saber Benharzallah
  • Nadjib Mesbahi
  • Abdelhak Merizig
  • Djamil Rezki

Abstract

Nowadays, the distributed computing plays an important role in the data mining process. To make systems scalable it is important to develop mechanisms that distribute the workload among several sites in a flexible way. Moreover, the acronym ERP refers to the systems and software packages used by organisations to manage day-by-day business activities. ERP systems are designed for the defined schema that usually has a common database. In this paper, we present a collaborative multi-agent based system for association rules mining from distributed databases. In our proposed approach, we combine the multi-agent system with association rules as a data mining technique to build a model that can execute the association rules mining in a parallel and distributed way from the centralised ERP database. The autonomous agents used to provide a generic and scalable platform. This will help business decision-makers to take the right decisions and provide a perfect response time using multi-agent system. The platform has been compared with the classic association rules algorithms and has proved to be more efficient and more scalable.

Suggested Citation

  • Merouane Zoubeidi & Okba Kazar & Saber Benharzallah & Nadjib Mesbahi & Abdelhak Merizig & Djamil Rezki, 2020. "A new approach agent-based for distributing association rules by business to improve decision process in ERP systems," International Journal of Information and Decision Sciences, Inderscience Enterprises Ltd, vol. 12(1), pages 1-35.
  • Handle: RePEc:ids:ijidsc:v:12:y:2020:i:1:p:1-35
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=104993
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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:ids:ijidsc:v:12:y:2020:i:1:p:1-35. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=306 .

    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.