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In order to adapt the hotel booking process to the unexpected changes that can occur at runtime, we present a methodological approach for the dynamic adaptation of service compositions according to the context. The proposed approach consists mainly of two steps: "the context management" step and "the adaptation actions" step.
The context management consists on the context modeling, capture and reasoning.
In this approach, the model is organized as a tree of model context which contain the components of the model.
The context processor component gathers context information from the user's environment performs context reasoning and generates context and situation events.
where [i.sub.0], [i.sub.1], ..., [i.sub.t] [member of] C, with [i.sub.0] = j being the beginning context and [i.sub.t] = k being the context at time t.
It is worth mentioning that, whatever policies are applied and whatever the output context is, if an adversary guesses that the user is in a sensitive context s [member of] C at time t, the probability that the guess result is true is at least P{[Z.sub.t] = s} because this probability can be computed by using (2).
The proper architectural support is an indispensible foundation for the gathering, modelling, storing, and distributing of context data.
Early in 2001, a noticeable example framework named Context Toolkit was developed [20].
We analyzed the context information which maybe produced in the new networking environment and the processes of service registration, service discovery, service selection, and service composition based on the needs of the user under the Internet of Things and defined the context information as the environment context information, the device context information, the user context information, and the calculation context information.
The original acquisition is the process of obtaining various types of context information (or data) directly from a variety of context information sources (such as sensors, RFID readers, and cameras).
