A Generalized Feature Model for Digital Twins

2026-03-06

AI Summary

This study addresses the absence of a unified model capable of consistently characterizing both core and optional features of digital twins. To bridge this gap, the authors propose, for the first time, a generic feature model encompassing the three canonical forms—digital models, digital shadows, and digital twins—derived through a systematic literature mapping. The model is empirically validated across three distinct application domains: emergency response, intelligent vehicles, and smart manufacturing. By providing a coherent foundation for design decisions, model-driven development, and test case generation in digital twin systems, the proposed framework significantly enhances development efficiency and model reusability.

Technology Category

• Machine Learning: Feature Construction/Reformulation
• Knowledge Representation and Reasoning: Description Logics
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• User Modeling, Personalization and Recommendation: User model development and evaluation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

The adoption of Digital Twin technologies is rapidly expanding in diverse industrial, economic, and societal domains. Over the past decade, a multitude of studies, surveys, and investigations have been conducted, examining the nature, applications, and advantages of Digital Twins. However, up until now, no proposal for a comprehensive feature model exists that effectively captures the mandatory and optional features of Digital Twins. To address this shortcoming, in this article, we present a general feature model for Digital Twins. Based on a systematic mapping study of existing literature, we developed a generalized feature model for Digital Models, Shadows, and Twins. To assess the validity of our proposed feature model, we have applied them to three use cases from the emergency, vehicular, and manufacturing domain. We conjecture that our proposed general feature model advances the field around Digital Twins by facilitating informed decision-making during design, enabling improved model-driven development of Digital Twins, and, eventually, fostering verification~\&~validation of Digital Twins by delivering a model-based foundation for test case inference.

Problem

• Digital Twin
• feature model
• model-driven development
• systematic mapping study
• verification and validation

Innovation

• Digital Twin
• Feature Model
• Model-Driven Development
• Systematic Mapping Study
• Verification and Validation