Andrea's research focus is on the application and development of formal methods in the digital twin context. She is a member of various research projects on digital twins, including
For more information on her research, projects, and publications have a look at here website.
Publications
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Kruger, Loes; Kobialka, Paul; Pferscher, Andrea; Johnsen, Einar Broch; Junges, Sebastian & Rot, Jurriaan
(2026).
Incremental Fingerprinting in an Open World.
Proceedings - IEEE Computer Security Foundations Symposium (CSF).
ISSN 1940-1434.
p. 1–16.
doi:
10.1109/CSF68417.2026.00001.
Full text in Research Archive
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Kobialka, Paul; Pferscher, Andrea; Aichernig, Bernhard K.; Johnsen, Einar Broch & Tarifa, Silvia Lizeth Tapia
(2026).
Automata Learning Versus Process Mining: The Case for User Journeys.
IEEE Transactions on Software Engineering.
ISSN 0098-5589.
52(6),
p. 1858–1874.
doi:
10.1109/tse.2026.3679253.
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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.
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Kamburjan, Eduard; Slaughter, Laura Ann; Johnsen, Einar Broch; Pferscher, Andrea & Weihl, Laura
(2025).
Towards Self-Adaptive Data Management in Digital Twins for Biodiversity Monitoring.
In Kessentini, Marouane; Sahraoui, Houari & Ali, Shaukat (Ed.),
2025 ACM/IEEE 28th International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C).
IEEE (Institute of Electrical and Electronics Engineers).
ISSN 9798331579906.
p. 257–263.
doi:
10.1109/models-c68889.2025.00044.
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Biodiversity monitoring is concerned with keeping track of different species in an ecosystem over time, with respect to their abundance, distribution and diversity. Environmental digital twins used for biodiversity monitoring share characteristics with industrial digital twins, but face additional challenges in connecting data and models: Biodiversity data is often not livestreamed, interventions are slow and require human interaction, and the scientific knowledge about species and their habitats constantly evolves. Today, environmental digital twins offer little automation support, or any support to help scientists link species observations to assumptions about biodiversity. This paper presents an application of structural self-adaptation, originally developed in the industrial domain, to environmental digital twins. We show how structural self-adaptation enables to autonomously adapt monitored assumptions to changes in the available data sources, and further discuss how digital twins can adapt to changes in the domain knowledge. A first evaluation is given based on underwater cameras in the Oslo Fjord.
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Kløvstad, Åsmund Aqissiaq Arild; Kobialka, Paul; Sieve, Riccardo; Pferscher, Andrea; Slaughter, Laura Ann & Tarifa, Silvia Lizeth Tapia
[Show all 7 contributors for this article]
(2025).
What-If Scenarios for the BedreFlyt Digital Twin.
In Bertrand, Nathalie; Dubslaff, Clemens & Klüppelholz, Sascha (Ed.),
Principles of Formal Quantitative Analysis: Essays Dedicated to Christel Baier on the Occasion of Her 60th Birthday.
Springer Nature.
ISSN 9783031974397.
p. 360–381.
doi:
10.1007/978-3-031-97439-7_18.
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Digital twin technology is emerging as a valuable tool for both short-term decision-making and long-term strategic planning across domains such as process industry, energy, space, transport, and healthcare. This paper reports on ongoing work in designing a digital twin to enhance resource planning in hospitals, e.g., for in-patient needs. Our focus here is on a novel technique to express what-if scenarios in digital twins to improve strategic planning processes, spanning, e.g., average-case and worst-case resource needs, expected patient treatments, and ranging over variations in available resources such as bed bays in the hospital ward. Due to the modularity of our digital twin architecture, different what-if scenarios can be explored simply by configuring the digital twin’s orchestrator, which triggers a formal methods analysis pipeline that combines executable formal models for simulation, optimization over constraints and a knowledge base that formalizes domain knowledge. We illustrate what-if scenario analysis in our digital twin architecture by considering the problem of bed bay allocation in a hospital ward.
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Johnsen, Einar Broch; Kobialka, Paul; Pferscher, Andrea & Tarifa, Silvia Lizeth Tapia
(2024).
Nudging Strategies for User Journeys: Take a Path on the Wild Side.
Lecture Notes in Computer Science (LNCS).
ISSN 0302-9743.
15230.
doi:
10.1007/978-3-031-73751-0_6.
Full text in Research Archive
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Kamburjan, Eduard; Pferscher, Andrea; Schlatte, Rudolf; Sieve, Riccardo; Tarifa, Silvia Lizeth Tapia & Johnsen, Einar Broch
(2024).
Semantic Reflection and Digital Twins: A Comprehensive Overview.
Lecture Notes in Computer Science (LNCS).
ISSN 0302-9743.
15240,
p. 129–145.
doi:
10.1007/978-3-031-73887-6_11.
Full text in Research Archive
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Aichernig, Bernhard K.; König, Sandra; Mateis, Cristinel; Pferscher, Andrea & Tappler, Martin
(2024).
Learning minimal automata with recurrent neural networks.
Software and Systems Modeling (SoSyM).
ISSN 1619-1366.
doi:
10.1007/s10270-024-01160-6.
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In this article, we present a novel approach to learning finite automata with the help of recurrent neural networks. Our goal is not only to train a neural network that predicts the observable behavior of an automaton but also to learn its structure, including the set of states and transitions. In contrast to previous work, we constrain the training with a specific regularization term. We iteratively adapt the architecture to learn the minimal automaton, in the case where the number of states is unknown. We evaluate our approach with standard examples from the automata learning literature, but also include a case study of learning the finite-state models of real Bluetooth Low Energy protocol implementations. The results show that we can find an appropriate architecture to learn the correct minimal automata in all considered cases.
View all works in NVA
Published
Sep. 3, 2024 11:12 AM
- Last modified
Feb. 9, 2025 7:00 PM
