Digital twins are gaining relevance in many domains to improve the operation and maintenance of complex systems. Despite their importance, most efforts are currently focused on their design, development, and deployment but do not fully address their validation. In this paper, we are interested in assessing the fidelity of physical and digital twins and, more specifically, whether they exhibit twinned behaviors. This will allow engineers to check the suitability of the digital twin for its intended purpose. Our approach assesses their fidelity by comparing the behavioral traces of the two twins. Our contribution is threefold. First, we define a measure of equivalence between individual snapshots capable of deciding whether two snapshots are sufficiently similar. Second, we use a trace alignment algorithm to align the corresponding equivalent states reached by the two twins. Finally, we measure the fidelity of the behavior of the two twins using the level of alignment achieved in terms of the percentage of matched snapshots and the distance between the aligned traces. Our proposal has been validated with the digital twins of four cyber-physical systems: an elevator, an incubator, a robotic arm, and a programmable robotic car. We were able to determine which systems were sufficiently faithful and which parts of their behavior failed to emulate their counterparts. Finally, we compared our proposal with similar approaches from the literature, highlighting their respective strengths and weaknesses related to our own.

There are numerous domains in which information systems need to deal with uncertain information. These uncertainties may originate from different reasons such as vagueness, imprecision, incompleteness, or inconsistencies, and in many cases, they cannot be neglected. In this article, we are interested in representing and processing uncertain information in domain models, considering the stakeholders’ beliefs (opinions). We show how to associate beliefs to model elements and how to propagate and operate with their associated uncertainty so that domain experts can individually reason about their models enriched with their personal opinions. In addition, we address the challenge of combining the opinions of different domain experts on the same model elements, with the goal to come up with informed collective decisions. We provide different strategies and a methodology to optimally merge individual opinions.

Mobile-based collaborative social computing applications represent an alternative to the server-centric models currently offered by major IT vendors, where users own the information they generate and take control over how other users access and exploit it. In this context, trust is fundamental for sharing information and making decisions based on the data collected from other users. This work develops a trust management system embedded in the Digital Avatars framework for collaborative social computing applications, using Subjective logic. It enables explicit representation and operation with trust information about both service providers (functional trust) and other users’ opinions about these providers (referral trust) in peer-to-peer environments. The proposal is specified using high- level models that can also be simulated and validated, and serve as a guide for the corresponding implementations in an existing social computing application platform. The proposed solution has been successfully applied in a collaborative carpooling system, where users need to trust other users with whom they share travels.