# Weaving Rules into Models@run.time for Embedded Smart Systems

## TL;DR

This paper introduces a novel method for embedding executable rules into data models with lazy loading, enabling smart systems to efficiently process large rule sets on limited hardware like Raspberry Pi.

## Contribution

It presents a new composition process that weaves rules into data models, improving reactivity and efficiency in resource-constrained environments.

## Key findings

- Handles large rule sets with low latency
- Works on restricted hardware like Raspberry Pi
- Effective in smart building case study

## Abstract

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.