An Agent-Based Approach for Extracting Business Association Rules from Centralized Databases Systems
Nadjib Mesbahi, Merouane Zoubeidi, Abdelhak Merizig, Okba Kazar
Journal of Digital Information Management, Vol. 17, No. 5, 2019.
DOI: https://doi.org/10.6025/jdim/2019/17/5/270-288
Link: http://dline.info/fpaper/jdim/v17i5/jdimv17i5_2.pdf
Abstract:
Today, enterprises use a variety of applications
to manage day-by-day business activities using a
large centralized database. Since a huge amount of data
stored in this centralized database produced by the daily
use of several systems, it is important to integrate decision-
making tools to analyse and interpret these business
data. For this purpose, Data Mining is a powerful
technology that promote information and knowledge extraction
from large databases. In this paper, we present
an agent-based approach for extracting business association
rules from centralized database systems. This
approach combine paradigm of multi-agent system and
the association rules as a data mining technique to build
anefficient model. It is relying on the intelligent partitioning
of data to make the execution of business association
rules in a parallel and distributed way from a large
centralized database. To validate our approach, we applied
it during the realization of a real case study on ERP
database at the National company of Well Services
(ENSP)using JADE platform with machine learning WEKA
toolbox for association rules mining. The developed system
has been compared with the classic association rules
algorithms and has proved it is more efficient and more
scalable. The main objective of our work is to improve
and accelerate the process of extracting association rules
by business through centralized database systems. As
a result, the decision process of these systems becomes
more improved.