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EnglishInteroperability 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.
| Titolo: | Inferring affordances using learning techniques | |
| Autori: | Bennaceur, A.; Johansson, Bo Richard; Moschitti, Alessandro; Spalazzese, R.; Sykes, D.; Saadi, R.; Issarny, V. | |
| Autori Unitn: | ||
| Titolo del volume contenente il saggio: | The First Workshop on Trustworthy Eternal Systems via Evolving Software, Data and Knowledge: EternalS’11 | |
| Luogo di edizione: | Budapest | |
| Casa editrice: | Springer | |
| Anno di pubblicazione: | 2011 | |
| Codice identificativo Scopus: | 2-s2.0-84865225851 | |
| ISBN: | 978-3-642-28032-0 | |
| Handle: | http://hdl.handle.net/11572/89068 | |
| Appare nelle tipologie: | 04.1 Saggio in atti di convegno (Paper in proceedings) |
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