Model-Driven Engineering Method to Support the Formalization of Machine Learning using SysML

TL;DR

This paper presents a model-driven engineering method using SysML to formalize machine learning tasks, enabling collaboration among domain experts and supporting data integration, processing, and documentation for industry applications.

Contribution

The work introduces a novel SysML-based approach for formalizing machine learning tasks, facilitating interdisciplinary collaboration and automating parts of the ML development process.

Findings

Validated with smart weather and waste prevention use cases

User study indicates improved understanding and usability

Supports semi-automatic code generation for ML tasks

Abstract

Methods: This work introduces a method supporting the collaborative definition of machine learning tasks by leveraging model-based engineering in the formalization of the systems modeling language SysML. The method supports the identification and integration of various data sources, the required definition of semantic connections between data attributes, and the definition of data processing steps within the machine learning support. Results: By consolidating the knowledge of domain and machine learning experts, a powerful tool to describe machine learning tasks by formalizing knowledge using the systems modeling language SysML is introduced. The method is evaluated based on two use cases, i.e., a smart weather system that allows to predict weather forecasts based on sensor data, and a waste prevention case for 3D printer filament that cancels the printing if the intended result cannot be achieved (image processing). Further, a user study is conducted to gather insights of potential users regarding perceived workload and usability of the elaborated method. Conclusion: Integrating machine learning-specific properties in systems engineering techniques allows non-data scientists to understand formalized knowledge and define specific aspects of a machine learning problem, document knowledge on the data, and to further support data scientists to use the formalized knowledge as input for an implementation using (semi-) automatic code generation. In this respect, this work contributes by consolidating knowledge from various domains and therefore, fosters the integration of machine learning in industry by involving several stakeholders.