Weaving Rules into Models@run.time for Embedded Smart Systems
This addresses the problem of high latency and memory constraints in embedded smart systems for domains like smart buildings, but it appears incremental as it builds on existing data model and rule-checking approaches.
The paper tackles the challenge of efficiently processing thousands of updates to trigger expert rules in smart systems on restricted hardware like Raspberry Pi, proposing a novel composition process that weaves executable rules into a data model with lazy loading abilities, and quantitatively shows it can handle big sets of rules on large-scale data models at low latency in a smart building case study.
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.