The 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 against 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.
IntroductionOne of the foremost challenges facing manufacturing industry nowadays is the largescale integration of their enterprise systems, along with their associated models, data, information, knowledge and web descriptions. To achieve a high level of integration of managerial and technical elements, companies are resorting to standard reference architectures and common enterprise models usually developed within international projects. On the other hand the enterprise systems generate large amounts of data which are a valuable asset and potentially an important source of new information and knowledge for improving the business of the enterprise, gaining competitive advantage in fierce markets and coping with changes and managerial complexity. Large organizations such as manufacturing companies could respond to changes and challenges in their business and production activities by using intensive and intelligent database processing to identify new trends and to predict and improve their business performance. Therefore knowledge discovery (KD) in databases is a promising solution and this paper is focused on using emerging knowledge technologies such as data mining for enterprise integration and modeling. However, a comprehensive and comparative review of literature related to knowledge discovery, data mining and enterprise integration and modeling approaches reveals that there is not any reported interdisciplinary research. This paper suggests that extending the existing enterprise modeling and integration architectures and environments to inco...
This paper presents a deep investigation and an interdisciplinary analysis of the collaborative networked enterprise engineering issues and modelling approaches related to the relevant aspects of the semantic web technology and knowledge strategies. The paper also suggests a novel framework based on ontology metamodelling, knowledge model discovery, and semantic web infrastructures, architectures, languages, and systems. The main aim of the research enclosed in this paper is to bridge the gaps between enterprise engineering, modelling, and especially networking by intensively applying semantic web technology based on ontology conceptual representations and knowledge discovery. The ontological modelling approaches together with knowledge strategies such as discovery (data mining) have become promising for future enterprise computing systems. The related reported research deals with the conceptual definition of a semantic-driven framework and a manufacturing enterprise metamodel (ME_M) using ontology, knowledge-driven object models, standards, and architectural approaches applied to collaborative networked enterprises. The conceptual semantic framework and related issues discussed in this paper may contribute towards new approaches of enterprise systems engineering and networking as well as applied standard and referenced ontological models.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.