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

Abstract

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.Index Terms-Composition of semantic web services, planning in artificial intelligence and case-based learning, INDYGO. I. INTRODUCTIONFrom the perspective of service composition, there are few approaches which integrate learning models to improve various aspects of the task of Web service. A particular model which proposes the use of planning in Web service composition is the concurrent planning and execution model (INDYGO) [1], [2], which Universidad Nacional at Medellin Campus designed and implemented, and aims to produce semantic web service compositions in real time while handling incomplete data. This model, like most service composers, carries out solutions without taking into account experiences of previous solutions. Due to this, it was deemed valuable to propose within the INDYGO model the implementation of case-based learning techniques to enable the improvement of its efficiency in matters concerning the composition process itself that the planner is in charge of. It is important to highlight that even though the model adjusts to a previous development, this proposal may also be easily adapted and implemented in other approaches which under AI planning techniques enable semantic Web service composition since it is in charge of formulating its solution relying on commonly known elements implemented within this dominion.To amplify the proposal, this document is organized as follows: section two revises the reference framework related Jaime Guzmán-Luna is with the Director of SINTELWEB Research Group, Universidad Nacional de Colombia, Medellin Campus (e-mail: jaguzman@unal.edu.co).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. II. A COMP...