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Integrating predictive analysis in self-adaptive pervasive systems

Ivan Dario Paez Anaya 1 
1 DiverSe - Diversity-centric Software Engineering
Inria Rennes – Bretagne Atlantique , IRISA-D4 - LANGAGE ET GÉNIE LOGICIEL
Abstract : In this thesis we proposed a proactive self-adaptation by integrating predictive analysis into two phases of the software process. At design time, we propose a predictive modeling process, which includes the activities: define goals, collect data, select model structure, prepare data, build candidate predictive models, training, testing and cross-validation of the candidate models and selection of the ''best'' models based on a measure of model goodness. At runtime, we consume the predictions from the selected predictive models using the running system actual data. Depending on the input data and the time allowed for learning algorithms, we argue that the software system can foresee future possible input variables of the system and adapt proactively in order to accomplish middle and long term goals and requirements.
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Submitted on : Wednesday, January 6, 2016 - 1:38:24 PM
Last modification on : Saturday, June 25, 2022 - 7:46:01 PM
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  • HAL Id : tel-01251551, version 1


Ivan Dario Paez Anaya. Integrating predictive analysis in self-adaptive pervasive systems. Other [cs.OH]. Université Rennes 1, 2015. English. ⟨NNT : 2015REN1S046⟩. ⟨tel-01251551⟩



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