Loughborough University
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An enterprise modeling and integration framework based on knowledge discovery and data mining

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journal contribution
posted on 2012-05-16, 11:10 authored by Elena I. Neaga, Jennifer HardingJennifer Harding
This paper deals with the conceptual design and development of an enterprise modeling and integration framework using knowledge discovery and data mining. First, the paper briefly presents the background and current state-of-the-art of knowledge discovery in databases and data mining systems and projects. Next, enterprise knowledge engineering is dealt with. The paper suggests a novel approach of utilizing existing enterprise reference architectures, integration and modeling frameworks by the introduction of new enterprise views such as mining and knowledge views. An extension and a generic exploration of the information view that already exists within some enterprise models are also proposed. The Zachman Framework for Enterprise Architecture is also outlined versus the existing architectures and the proposed enterprise framework. The main contribution of this paper is the identification and definition of a common knowledge enterprise model which represents an original combination between the previous projects on enterprise architectures and the Object Management Group (OMG) models and standards. The identified common knowledge enterprise model has therefore been designed using the OMG's Model-Driven Architecture (MDA) and Common Warehouse MetaModel (CWM), and it also follows the RM-ODP (ISO/OSI). It has been partially implemented in Java(TM), Enterprise JavaBeans (EJB) and Corba/IDL. Finally, the advantages and limitations of the proposed enterprise model are outlined.



  • Mechanical, Electrical and Manufacturing Engineering


NEAGA, E.I. and HARDING, J.A., 2005. An enterprise modeling and integration framework based on knowledge discovery and data mining. International Journal of Production Research, 43 (6), pp. 1089-1108.


© Taylor and Francis


  • AM (Accepted Manuscript)

Publication date



This article was accepted for publication in the journal International Journal of Production Research [© Taylor and Francis]. The definitive version is available at: http://dx.doi.org/10.1080/00207540412331322939






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