Middleware Frameworks for Ubiquitous Computing Environment

For decades, software engineering continued to be an evolving discipline. Software developers are still after software development approaches that achieve reusability at more levels of abstraction. This is particularly true for developing applications for the enterprise; where business collaborations are a central concern. Software designers need a modeling framework that enables process engineering by assembling reusable services. Moreover, the development approach must enable parts of the enterprise to react quickly and reliably to changes in requirements and in available technologies. In this paper, we are seeking to improve the workload efficiency and inference capability of context-aware process in real-time data processing middleware core of ubiquitous environment. We propose an architecture that apply a neural network model. Ubiquitous environment should cope with the requirements of context-aware by accepting a lot of information collected from USN(Ubiquitous Sensor Network) and inference. There exists a conventional context-aware method that extracts if-then rule and constructs a rule database. The conventional context-aware method can not recognize a situation when database is failed. Moreover, as rules get complicated, the performance of the method is reduced. This paper suggests the rule-based context-aware neural network model to solve these problems, and the empirical results are shown.