Weaving Rules into Models@run.time for Embedded Smart Systems
English
Mouline, Ludovic[University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Hartmann, Thomas[University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Fouquet, François[University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Le Traon, Yves[University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Bourcier, Johann[University of Rennes 1 > Irisa > DiverSE > Associate Professor]
Barais, Olivier[University of Rennes 1 > Irisa > DiverSE > Professor]
Apr-2017
Programming '17 Companion to the first International Conference on the Art, Science and Engineering of Programming
Mouline, Ludovic
Hartmann, Thomas
Fouquet, François
Le Traon, Yves
Bourcier, Johann
Barais, Olivier
ACM
Yes
International
978-1-4503-4836-2
Brussels
Belgium
Second International Modularity in Modelling Workshop
from 03-04-2017 to 04-04-2017
Brussels
Belgium
[en] Models@run.time ; Reactive systems ; Rule engines ; Lazy loading ; Smart systems ; Embedded Systems
[en] Smart systems are characterised by their ability to analyse measured data in live and to react to changes according to expert rules. Therefore, such systems exploit appropriate data models together with actions, triggered by domain-related conditions. The challenge at hand is that smart systems usually need to process thousands of updates to detect which rules need to be triggered, often even on restricted hardware like a Raspberry Pi. Despite various approaches have been investigated to efficiently check conditions on data models, they either assume to fit into main memory or rely on high latency persistence storage systems that severely damage the reactivity of smart systems. To tackle this challenge, we propose a novel composition process, which weaves executable rules into a data model with lazy loading abilities. We quantitatively show, on a smart building case study, that our approach can handle, at low latency, big sets of rules on top of large-scale data models on restricted hardware.