Applying Case-Based Learning to Improve the Efficiency in the Web Service Compositions

Abstract

Several planning techniques in artificial intelligence have been used to perform web service composition (semantic or not), but this process typically uses heuristics based planners combined with search techniques usually too expensive in time solution. In this article, we propose the use of case-based reasoning to reduce the computation times of composition; the model aims to infer from past experience a solution that would guide the selection process during a Web services composition. The proposed methodology also uses a classification defined by an algorithm of semantic similarity technique, in order to compare the new problem, with all previous problems. The previous problem with greatest similarity is accompanied by its corresponding solution and is used to specify which goals already achieved and what remain to achieve for the new problem. The result demonstrates greater efficiency, reducing the search space spending less time. to the problem of service composition using AI planning techniques, specifically, centered on INDYGO. Section three, discusses and analyzes case-based learning concepts and proposes a representation of the solution starting from them; section four details the integration architecture of the CBR and INDYGO model and its functionality. Section five summarizes some results of the validation of the model, and section six presents conclusions and future work related to this proposal.