Enhancing legacy services through context-enriched sensor data
Today' services found in the Internet and made available to a broad number of users already provide a wide variety of options, but still lack the use of user specific context data. Information, such as current location, situation, or people around the user, can help to improve advanced search services like Yahoo or Google even further. However, this usually implies that each of these services must retrieve and store great amount of private data for each user. This does not only impose technical challenges, but also a huge number of privacy issues. We are therefore proposing a framework that derives legacy services with input that describes the context of the current user also taking into account his or her and the companions' preferences. Furthermore, this framework is able to adapt the output of these services according to the current user context and to utilize user feedback to iteratively refine the service results further. Thereby, our Context-aware Service Adaptation Framework (CaSAF) is able to render existing legacy services context-aware without affecting the services implementation taking into account various sensor data available to the user.