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

Merouane Zoubeidi, O. Kazar, Saber Benharzallah, Nadjib Mesbahi, Abdelhak Merizig, Djamil Rezki
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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.
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提出了一种基于agent的企业关联规则分布方法,以改善ERP系统的决策过程
如今,分布式计算在数据挖掘过程中扮演着重要的角色。为了使系统具有可伸缩性,开发以灵活的方式在多个站点之间分配工作负载的机制非常重要。此外,首字母缩略词ERP指的是组织用来管理日常业务活动的系统和软件包。ERP系统是为通常具有公共数据库的已定义模式而设计的。本文提出了一种基于多智能体的分布式数据库关联规则挖掘系统。在我们提出的方法中,我们将多智能体系统与关联规则作为一种数据挖掘技术相结合,建立了一个模型,该模型可以从集中式ERP数据库中以并行和分布式的方式执行关联规则挖掘。用于提供通用和可扩展平台的自主代理。这将有助于业务决策者做出正确的决策,并使用多代理系统提供完美的响应时间。通过与经典关联规则算法的比较,证明该平台具有更高的效率和可扩展性。
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