A Service Reasoning Model based on Uncertain Input Parameters

In pervasive computing environment, the inherent dynamic quality often makes the input parameters uncertain, so it is difficult to offer a suitable service according to this situation. In order to resolve the difficulty and enhance the service´s auto-adaptability, the paper presents the uncertain input parameters service model. Through using the Smith Waterman algorithm to compare with the uncertain input parameters chain and the standard task chain, the model can reason out the most similar standard task chain, which triggers the model to offer the suitable service. The course includes the following steps, such as the forming process of the input parameters chain, the formulation of score matrix, the judgment of the final result and so on. At last an application scenario is written up to demonstrate the feasibility of the algorithm and the advantage of the model.