Interoperability among the multitude of heterogeneous and evolving networked systems made available as black boxes remains a tough challenge. Learning technology is increasingly employed to extract behavioural models that form the basis for systems of systems integration. However, as networked systems evolve, their learned models need to evolve as well. This can be achieved by collecting actual interactions via monitoring and using these observations to continuously refine the learned behavioural models and, in turn, the overall system. This approach is part of the overall CONNECT approach.

Never-stop Learning: continuous validation of learned models for evolving systems through monitoring

Bertolino A.;Calabro' A.;
2012

Abstract

Interoperability among the multitude of heterogeneous and evolving networked systems made available as black boxes remains a tough challenge. Learning technology is increasingly employed to extract behavioural models that form the basis for systems of systems integration. However, as networked systems evolve, their learned models need to evolve as well. This can be achieved by collecting actual interactions via monitoring and using these observations to continuously refine the learned behavioural models and, in turn, the overall system. This approach is part of the overall CONNECT approach.
2012
Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" - ISTI
Inglese
88
28
29
Sì, ma tipo non specificato
Evolving Systems
Web services
Monitoring
Learning
Grant agreement231167 Tipo ProgettoEU_FP7
1
4
info:eu-repo/semantics/article
262
Bertolino, A.; Calabro', A.; Merten, M.; Steffen, B.
01 Contributo su Rivista::01.01 Articolo in rivista
open
   Emergent Connectors for Eternal Software Intensive Networked Systems
   CONNECT
   FP7
   231167
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/257512
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