Automatic Service Categorisation through Machine Learning in Emergent Middleware

Abstract : The modern environment of mobile, pervasive, evolving ser- vices presents a great challenge to traditional solutions for enabling in- teroperability. Automated solutions appear to be the only way to achieve interoperability with the needed level of flexibility and scalability. While necessary, the techniques used to determine compatibility, as a precursor to interaction, come at a substantial computational cost, especially when checks are performed between systems in unrelated domains. To over- come this, we apply machine learning to extract high-level functionality information through text categorisation of a system's interface descrip- tion. This categorisation allows us to restrict the scope of compatibility checks, giving an overall performance gain when conducting matchmak- ing between systems. We have evaluated our approach on a corpus of web service descriptions, where even with moderate categorisation accuracy, a substantial performance benefit can be found. This in turn improves the applicability of our overall approach for achieving interoperability in the Connect project.
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Communication dans un congrès
Bernhard Beckert and Ferruccio Damiani and Frank S. de Boer and Marcello M. Bonsangue. FMCO 2011 - 10th International Symposium on Formal Methods for Components and Objects, Oct 2011, Turin, Italy. Springer, 7542, pp.133-149, 2011, Lecture Notes in Computer Science. 〈10.1007/978-3-642-35887-6_7〉
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Amel Bennaceur, Valérie Issarny, Johansson Richard, Moschitti Alessandro, Spalazzese Romina, et al.. Automatic Service Categorisation through Machine Learning in Emergent Middleware. Bernhard Beckert and Ferruccio Damiani and Frank S. de Boer and Marcello M. Bonsangue. FMCO 2011 - 10th International Symposium on Formal Methods for Components and Objects, Oct 2011, Turin, Italy. Springer, 7542, pp.133-149, 2011, Lecture Notes in Computer Science. 〈10.1007/978-3-642-35887-6_7〉. 〈hal-00718655〉

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