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.

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Notwithstanding the significant advances in contextaware computing in pervasive computing and self-adaptive systems, there is still much more to be desired in providing better context services. The number of sensors deployed world-wide increases very rapidly. The Internet of Things, amongst others, generates vast amounts of data of many different data types. How data are used is essential to improve user experience and efficiencies of the systems in which they occur. We explain how familiar concepts of Process Mining strengthen generalised sensor context services. We present a laboratory case to explain the approach. By way of a real-world example, we confirm the viability of using Process Mining to strengthen context-aware computing.

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