Paul Kobialka

Digital Twins (DTs) are increasingly used to manage systems under fluctuating demand, yet many remain static and cannot adjust their internal models or control policies as the targeted real system changes. We present a self-adaptive DT architecture that supports runtime reconfiguration of resources. The architecture integrates semantic reflection to keep the DT’s runtime model aligned with the structural representation of the real system, lifecycle-based state management to trigger reconfiguration at appropriate times, and penalty-guided optimisation for decision-making under constraints, balancing resilience and operational cost when capacity must be reorganised. We realise the approach in DynResDT, a resilient hospital ward prototype for bed bay allocation of patients. Through simulation with realistic patient-arrival patterns and stress peaks, the DT maintains model consistency, opens and closes overflow capacity when needed, and allocates patients while minimising costly room usage and unnecessary moves. Our results show a practical trade-off between correctness and responsiveness: timely and principled adaptions offset potential overhead through reflection and lifecycle logic. The architectural pattern is applicable beyond healthcare to other dynamic resource-management domains.

With the servitization of business, understanding how users experience services becomes a crucial success factor for companies. Therefore, there is a need to include feedback from user experiences in the software engineering process. Behavioral models of user journeys, describing how users experience their interaction with a service, can provide insights and potentially improve services. In this paper, we investigate techniques that allow the automatic generation of behavioral models from user interactions with a service, recorded in an event log. We first compare two established techniques that generate behavioral models from a given event log: automata learning and process mining. Afterward, we present a novel, hybrid method that combines both automata learning and process mining methods to overcome their limitations. For the existing techniques, we present methods to learn models of user journeys and evaluate the accuracy of the resulting models. We then compare these techniques with our novel method for the automatic extraction of user journey models from the event logs of digital services. We assess the practical applicability of all techniques by evaluating real-world applications. Our results show that process mining techniques rely on expert knowledge, while automata learning techniques depend on the distribution of events in the given event log. We further show that the proposed hybrid technique combines the strengths of both process mining and automata learning, automatically selecting the best method and parameter settings for a given event log to learn very accurate models.

Network protocol fingerprinting is used to identify a protocol implementation by analyzing its input-output behavior. Traditionally, fingerprinting operates under a closed-world assumption, where models of all implementations are assumed to be available. However, this assumption is unrealistic in practice. When this assumption does not hold, fingerprinting results in numerous misclassifications without indicating that a model for an implementation is missing. Therefore, we introduce an open-world variant of the fingerprinting problem, where not all models are known in advance. We propose an incremental fingerprinting approach to solve the problem by combining active automata learning with closed-world fingerprinting. Our approach quickly determines whether the implementation under consideration matches an available model using fingerprinting and conformance checking. If no match is found, it learns a new model by exploiting the structure of available models. We prove the correctness of our approach and improvements in asymptotic complexity compared to naive baselines. Moreover, experimental results on a variety of protocols demonstrate a significant reduction in misclassifications and interactions with these black-boxes.

Service providers may systematically record and analyze how users interact with a service. User journeys model this interaction from the user’s perspective. We can think of user journeys in terms of multi-party event logs in which events are triggered by two independent parties, the user and the service provider, both controlling their share of actions. Multi-party event logs enable the automated construction of weighted two-player games, which can be used to analyze the user journeys. However, the games constructed for complex user journeys may get large, which makes visual understanding challenging. To reduce the size of these games, we develop an analysis technique for a game’s decision boundary , at which the outcome of the game is determined. Decision boundaries identify subgames that are equivalent to the full game with respect to the final outcome of user journeys. The paper formalizes decision boundaries for user journey games with history refinements, and studies properties of decision boundaries that simplify their analysis. The decision boundary analysis from multi-party event logs has been implemented in a modular tool, which can be connected to existing process mining pipelines, and evaluated on benchmark event logs revealing changes in the user journey and the interaction between users and service provider.

Digital twins are emerging as a valuable tool for short-term decision-making as well as for long-term strategic planning across numerous domains, including process industry, energy, space, transport, and healthcare. This paper reports on our ongoing work on designing a digital twin to enhance resource planning, e.g., for the in-patient ward needs in hospitals. By leveraging executable formal models for system exploration, ontologies for knowledge representation and an SMT solver for constraint satisfiability, our approach aims to explore hypothetical "what-if" scenarios to improve strategic planning processes, as well as to solve concrete, short-term decision-making tasks. Our proposed solution uses the executable formal model to turn a stream of arriving patients, that need to be hospitalized, into a stream of optimization problems, e.g., capturing daily inpatient ward needs, that can be solved by SMT techniques. The knowledge base, which formalizes domain knowledge, is used to model the needed configuration in the digital twin, allowing the twin to support both short-term decisionmaking and long-term strategic planning by generating scenarios spanning average-case as well as worst-case resource needs, depending on the expected treatment of patients, as well as ranging over variations in available resources, e.g., bed distribution in different rooms. We illustrate our digital twin architecture by considering the problem of bed bay allocation in a hospital ward.

Industry is shifting towards service-based business models, for which user satisfaction is crucial. User satisfaction can be analyzed with user journeys, which model services from the user’s perspective. Today, these models are created manually and lack both formalization and tool-supported analysis. This limits their applicability to complex services with many users. Our goal is to overcome these limitations by automated model generation and formal analyses, enabling the analysis of user journeys for complex services and thousands of users. In this paper, we use stochastic games to model and analyze user journeys. Stochastic games can be automatically constructed from event logs and model checked to, e.g., identify interactions that most effectively help users reach their goal. Since the learned models may get large, we use property-preserving model reduction to visualize users’ pain points to convey information to business stakeholders. The applicability of the proposed method is here demonstrated on two complementary case studies.

The servitization of business is moving industry to business models driven by customer demand. Customer satisfaction is connected with financial rewards, forcing companies to invest in their users’ experience. User journeys describe how users maneuver through a service. Today, user journeys are typically modeled graphically, and lack formalization and analysis support. This paper proposes a formalization of user journeys as weighted games between the user and the service provider and a systematic data-driven method to derive these user journey games from system logs, using process mining techniques. As the derived games may contain cycles, we define an algorithm to transform user journeys games with cycles into acyclic weighted games, which can be model checked using "Image missing" to uncover potential challenges in a company’s interactions with its users and derive company strategies to guide users through their journeys. Finally, we propose a user journey sliding-window analysis to detect changes in the user journey over time by model checking a sequence of generated games. Our analysis pipeline has been evaluated on an industrial case study; it revealed design challenges within the studied service and could be used to derive actionable recommendations for improvement.