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

Authors: Merouane Zoubeidi; Okba Kazar; Saber Benharzallah; Nadjib Mesbahi; Abdelhak Merizig; Djamil Rezki

Addresses: LINFI Laboratory, Computer Science Department, University of Biskra, 07000, Biskra, Algeria; National Well Services Company, A Sonatrach Company, Hassi-Messaoud, Algeria ' LINFI Laboratory, Computer Science Department, University of Biskra, 07000, Biskra, Algeria ' LINFI Laboratory, Computer Science Department, Batna 2 University, 05000, Batna, Algeria ' LINFI Laboratory, Computer Science Department, University of Biskra, 07000, Biskra, Algeria; National Well Services Company, A Sonatrach Company, Hassi-Messaoud, Algeria ' LINFI Laboratory, Computer Science Department, University of Biskra, 07000, Biskra, Algeria ' LAP Laboratory, Industrial Engineering Department, Batna 2 University, 05000, Batna, Algeria; National Well Services Company, A Sonatrach Company, Hassi-Messaoud, Algeria

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.

Keywords: enterprise resource planning; ERP; multi-agents system; MAS; data mining associate rules; JADE; WEKA.

DOI: 10.1504/IJIDS.2020.104993

International Journal of Information and Decision Sciences, 2020 Vol.12 No.1, pp.1 - 35

Received: 01 May 2018
Accepted: 23 Aug 2018
Published online: 10 Feb 2020 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article