Context-Aware Data Mining Methodology for Supply Chain Finance Cooperative Systems

Context-awareness computing work has been carried out by many researchers. However, little has been done in building a context-aware data mining methodology which supports decision-making based on an enterprise supply chain finance cooperative systems. An enterprise supply chain finance cooperative system that interacts with bank, buyer and supplier environment may not have sufficient knowledge of the environment, and it is the responsibility of the environment observation units to communicate the environment context parameters to the system. Context-aware data mining is an application able to sense and analyze the context from various sources and which takes actions suited to contexts for improving performance and efficacy of decision-making by identifying the unknown factors. This paper proposes a context-aware data mining methodology for enterprise supply chain finance cooperative system for manufacturing industry. It consists of three main components. The first component is the model for enterprise supply chain finance cooperative systems with cooperative agents serving as the representatives of bank, buyer, supplier communicating with each other over the company´s network. In the second component, context-aware data mining framework is proposed based on the architecture for the enterprise supply chain finance cooperative systems. The third component is a system which has been designed and implemented for the context-aware data mining methodology.