Web service composition using input/output dependency matrix
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Automation of Web service composition i.e., the process of forming new value added Web services is one of the most promising challenges in Web service research area. Such an automation does not involve only a richer semantic for Web service description but also reasoning processes about those descriptions. Semantics is supposed to be one of the key elements for the automation of Web service composition. Indeed semantic Web services enable a rich machineunderstandable descriptions of their capabilities and processes in order to ease automation of processes such as discovery, selection and composition. In this paper one studies the functional level description of Web services and its impact on Web service composition hence the presentation of a formal model CLM (Causal link matrix). The model supports a semantic context in order to find a correct, complete and consistent plan as a AI planning-based composition. The innovative and formal model follows a forward chaining-oriented composition. Moreover two methods of optimization (i.e., local-oriented and a global-oriented) of Web service composition are introduced to discover the best plan according to a semantic criterion i.e., the causal link between Web services.