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Inferring affordances using learning techniques

Bennaceur, Amel; Johansson, Richard; Moschitti, Alessandro; Spalazzese, Romina; Sykes, Daniel; Saadi, Rachid and Issarny, Valérie (2012). Inferring affordances using learning techniques. In: Eternal Systems, Springer, pp. 79–87.

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Interoperability among heterogeneous systems is a key challenge in today’s networked environment, which is characterised by continual change in aspects such as mobility and availability. Automated solutions appear then to be the only way to achieve interoperability with the needed level of flexibility and scalability. While necessary, the techniques used to achieve interaction, working from the highest application level to the lowest protocol level, come at a substantial computational cost, especially when checks are performed indiscriminately between systems in unrelated domains. To overcome this, we propose to use machine learning to extract the high-level functionality of a system and thus restrict the scope of detailed analysis to systems likely to be able to interoperate.

Item Type: Conference or Workshop Item
Copyright Holders: 2011 Springer-Verlag
ISBN: 3-642-28032-3, 978-3-642-28032-0
ISSN: 1865-0929
Project Funding Details:
Funded Project NameProject IDFunding Body
Connect: Emergent Connectors for Eternal Software Intensive Networked Systems231167Not Set
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Related URLs:
Item ID: 39469
Depositing User: Amel Bennaceur
Date Deposited: 10 Feb 2014 10:00
Last Modified: 19 Dec 2017 10:32
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