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A Model-Based Architecture For Adaptive And Scalable Integration Of Enterprise Systems

Abstract

The integration and management of multiple data and knowledge systems entails not only interoperability, local autonomy, and concurrent processing, but also the new capabilities of adaptiveness allowing scalable (or incremental) integration and non-standard or legacy systems, while avoiding the well-known performance problems caused by the traditional approaches to global control and management. The past decade has seen many good solutions developed from vast worldwide efforts for many aspects of the problem: yet certain needs remain unsatisfied - especially the new capabilities of adaptiveness. Most of the previous efforts have focused either on providing an architecture for direct interoperation among different systems (such as COBRA, Common Object Request Broker Architecture), or on developing a global model to manage these systems in the tradition of databases (such as heterogeneous and distributed DBMS's). It seems that a promising approach to the problem is making the interoperable architecture adaptive and scalable by virtue of defining it through the global model; or, simply, the model-based architecture.

Such an architecture is proposed in this paper, using the metadatabase model. The concept of metadata independence for multiple systems is presented as the basis for the conceptual design of the architecture, while the Rule-Oriented Programming Environ ment (ROPE) method is developed to execute the design. ROPE implements global (integration) knowledge into localized rule-oriented shells which constitute the control backbone of the interoperating architecture, and rule-oriented shells which constitute the control backbone of the interoperating architecture, and manages theses shells. The metadatabase enables the shells to grow in number as well as to change its control knowledge contents. A rpototype is developed to test the concept and design.

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