Mobile applications often adapt their behavior according to user context, however, they are often limited to consider few sources of contextual information, such as user position or language. This article reviews existing work in context-aware systems (CAS), e.g., how to model context, and discusses further development of CAS and its potential applications by looking at available information, methods and technologies. Social Media seems to be an interesting source of personal information when appropriately exploited. In addition, there are many types of general information, ranging from weather and public transport to information of books and museums. These information sources can be combined in previously unexplored ways, enabling the development of smarter mobile services in different domains. Users are, however, reluctant to provide their personal information to applications; therefore, there is a crave for new regulations and systems that allow applications to use such contextual data without compromising the user privacy.

Acknowledging the user context, e.g., position and activity, provides a natural way to adapt applications according to the user needs. How to actually capture and exploit context, however, is not self-evident and it is tempting to assign the related responsibilities to individual context-consuming applications. Unfortunately, this confuses the user, complicates application development and hinders context-aware semantic computing as a research discipline. In this article, we outline context-aware semantic computing research topics and the state-of-the-art mobile application development frameworks of special interest to us, acknowledging best practices for accessing and modeling sensor context. From the integrated point of view, context-aware semantic computing is demonstrated in terms of a software component called context engine. In order to better understand how theory is tied with practice, we also introduce a simple context engine prototype. Finally, we use the research background and the empirical setting to discuss the significant problems and relevant research directions in context-aware semantic processing.

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