This paper aims to propose an extensible, service-oriented framework for context-aware data acquisition, description, interpretation and reasoning, which facilitates the development of mobile applications that provide a context-awareness service.
First, the authors propose the context data reasoning framework (CDRFM) for generating service-oriented contextual information. Then they used this framework to composite mobile sensor data into low-level contextual information. Finally, the authors exploited some high-level contextual information that can be inferred from the formatted low-level contextual information using particular inference rules.
The authors take “user behavior patterns” as an exemplary context information generation schema in their experimental study. The results reveal that the optimization of service can be guided by the implicit, high-level context information inside user behavior logs. They also prove the validity of the authors’ framework.
Further research will add more variety of sensor data. Furthermore, to validate the effectiveness of our framework, more reasoning rules need to be performed. Therefore, the authors may implement more algorithms in the framework to acquire more comprehensive context information.
CDRFM expands the context-awareness framework of previous research and unifies the procedures of acquiring, describing, modeling, reasoning and discovering implicit context information for mobile service providers.
Support the service-oriented context-awareness function in application design and related development in commercial mobile software industry.
Extant researches on context awareness rarely considered the generation contextual information for service providers. The CDRFM can be used to generate valuable contextual information by implementing more reasoning rules.
