Development of an intelligent data-mining system for a dispersed manufacturing network

Recent advances related to on-line analytical processing (OLAP) have resulted in a significant improvement in data analysis efficiency by virtue of its multidimensional database structure and pre-computing operations of measuring data. However, the research related to the design and implementation of OLAP, particularly in the support of dispersed manufacturing networks in terms of 'intelligent decision making', has yet to be considered as remarkable. Research studies indicate that the level of intelligence of decision support systems can be enhanced with the incorporation of computational intelligence techniques such as case-based reasoning or rule-based reasoning. This paper describes the development of an intelligent data-mining system using a rule-based OLAP approach which can be adopted to support dispersed manufacturing networks in terms of performance enhancement. In this paper, the techniques, methods and infrastructure for the development of such a data-mining system, which possesses certain intelligent features, are presented. To validate the feasibility of this approach, a case example related to the testing of the approach in an emulated industrial environment is covered.