Integrating Data Mining into Feedback Loops for Predictive Context Adaptation

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dc.contributor.authorRook, Angela
dc.contributor.authorKnauss, Alessia
dc.contributor.authorDamian, Daniela
dc.contributor.authorMuller, Hausi A.
dc.contributor.authorThomo, Alex
dc.date.accessioned2013-11-29T21:58:44Z
dc.date.available2013-11-29T21:58:44Z
dc.date.copyright2013en_US
dc.date.issued2013-11-29
dc.description.abstractRequirements for today's systems are increasingly valid only within certain operating contexts. Requirements engineering and implementation stages of system development must carefully consider how to integrate evolving context related to specific requirements in order for the system to stay relevant and flexible. In this paper we propose to use data mining techniques for predictive context adaptation. Our approach leverages data collected from the past and decides, based on this historical data, which context conditions to monitor in order to predictively identify when a system needs to be adapted to fulfill a particular requirement. We demonstrate our approach on an adaptive mobile application to support the coordination of a team of rowers in an environment with a continually changing operational context.en_US
dc.description.reviewstatusUnrevieweden_US
dc.description.scholarlevelFacultyen_US
dc.identifier.urihttp://hdl.handle.net/1828/5050
dc.language.isoenen_US
dc.subject.departmentDepartment of Computer Science
dc.titleIntegrating Data Mining into Feedback Loops for Predictive Context Adaptationen_US
dc.typeTechnical Reporten_US

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