Dealing with uncertainty in contextual requirements at runtime: A proof of concept
This work presents SACRE, a proof-of-concept implementation of an existing approach, ACon. ACon uses a feedback loop to detect contextual requirements affected by uncertainty and data mining techniques to determine the best operationalization of contexts on top of sensed data.
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2015 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/77761 |
| Acceso en línea: | https://hdl.handle.net/2117/77761 |
| Access Level: | acceso abierto |
| Palabra clave: | Self-adaptive software self-adaptive systems contextual requirements data mining smart vehicles drowsy drivers Programari autoadaptable Àrees temàtiques de la UPC::Informàtica |
| Sumario: | This work presents SACRE, a proof-of-concept implementation of an existing approach, ACon. ACon uses a feedback loop to detect contextual requirements affected by uncertainty and data mining techniques to determine the best operationalization of contexts on top of sensed data. |
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