# A Model-Driven Approach to Machine Learning and Software Modeling for the IoT

arXiv: https://arxiv.org/abs/2107.02689 · HTML: https://mlsanity.com/paper/2107.02689/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 61 references that are in the map.
- First posted on arXiv 6 Jul 2021.

## Builds on (most cited first, 5 of 5)

- [MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems](https://mlsanity.com/paper/1512.01274/) (2015)
- [Theano: A Python framework for fast computation of mathematical expressions](https://mlsanity.com/paper/1605.02688/) (2016)
- [From Things' Modeling Language (ThingML) to Things' Machine Learning (ThingML2)](https://mlsanity.com/paper/2009.10632/) (2020)
- [ThingML+ Augmenting Model-Driven Software Engineering for the Internet of Things with Machine Learning](https://mlsanity.com/paper/2009.10633/) (2020)
- [GreyCat: Efficient What-If Analytics for Data in Motion at Scale](https://mlsanity.com/paper/1803.09627/) (2018)

## Built on by (most cited first, 4 of 4)

- [Model-driven Engineering for Machine Learning Components: A Systematic Literature Review](https://mlsanity.com/paper/2311.00284/) (2023)
- [Model-Driven Quantum Federated Learning (QFL)](https://mlsanity.com/paper/2304.08496/) (2023)
- [Bridging MDE and AI: A Systematic Review of Domain-Specific Languages and Model-Driven Practices in AI Software Systems Engineering](https://mlsanity.com/paper/2307.04599/) (2023)
- [MDE for Machine Learning-Enabled Software Systems: A Case Study and Comparison of MontiAnna & ML-Quadrat](https://mlsanity.com/paper/2209.07282/) (2022)

## Abstract (arXiv)

Models are used in both Software Engineering (SE) and Artificial Intelligence (AI). SE models may specify the architecture at different levels of abstraction and for addressing different concerns at various stages of the software development life-cycle, from early conceptualization and design, to verification, implementation, testing and evolution. However, AI models may provide smart capabilities, such as prediction and decision-making support. For instance, in Machine Learning (ML), which is currently the most popular sub-discipline of AI, mathematical models may learn useful patterns in the observed data and can become capable of making predictions. The goal of this work is to create synergy by bringing models in the said communities together and proposing a holistic approach to model-driven software development for intelligent systems that require ML. We illustrate how software models can become capable of creating and dealing with ML models in a seamless manner. The main focus is on the domain of the Internet of Things (IoT), where both ML and model-driven SE play a key role. In the context of the need to take a Cyber-Physical System-of-Systems perspective of the targeted architecture, an integrated design environment for both SE and ML sub-systems would best support the optimization and overall efficiency of the implementation of the resulting system. In particular, we implement the proposed approach, called ML-Quadrat, based on ThingML, and validate it using a case study from the IoT domain, as well as through an empirical user evaluation. It transpires that the proposed approach is not only feasible, but may also contribute to the performance leap of software development for smart Cyber-Physical Systems (CPS) which are connected to the IoT, as well as an enhanced user experience of the practitioners who use the proposed modeling solution.