Inferring affordances using learning techniques

Amel Bennaceur, Richard Johansson, Alessandro Moschitti, Romina Spalazzese, Daniel Sykes, Rachid Saadi, Valérie Issarny

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationEternal Systems - First International Workshop, EternalS 2011, Revised Selected Papers
Pages79-87
Number of pages9
DOIs
Publication statusPublished - 27 Aug 2012
Event1st International Workshop on Eternal Systems, EternalS 2011 - Budapest, Hungary
Duration: 3 May 20113 May 2011

Publication series

NameCommunications in Computer and Information Science
Volume255 CCIS
ISSN (Print)1865-0929

Other

Other1st International Workshop on Eternal Systems, EternalS 2011
CountryHungary
CityBudapest
Period3/5/113/5/11

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ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

Cite this

Bennaceur, A., Johansson, R., Moschitti, A., Spalazzese, R., Sykes, D., Saadi, R., & Issarny, V. (2012). Inferring affordances using learning techniques. In Eternal Systems - First International Workshop, EternalS 2011, Revised Selected Papers (pp. 79-87). (Communications in Computer and Information Science; Vol. 255 CCIS). https://doi.org/10.1007/978-3-642-28033-7_7