02063nas a2200169   4500008004100000245010600041210006900147260001200216300001200228490000700240520150900247100002001756700002301776700002201799700001601821856005601837       2019                        eng d00aAn Agent-Based Approach for Extracting Business Association Rules from Centralized Databases Systems-0 aAgentBased Approach for Extracting Business Association Rules fr  c10/2019  a270-2880 v173 a<p>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.</p>
1 aMesbahi, Nadjib1 aZoubeidi, Merouane1 aMerizig, Abdelhak1 aKazar, Okba  uhttp://dline.info/fpaper/jdim/v17i5/jdimv17i5_2.pdf