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19th International Conference on Software Technologies, ICSOFT 2024

作者： Lazarova-Molnar, Sanja Institute of Applied Informatics and Formal Description Methods Karlsruhe Institute of Technology Karlsruhe Germany Mærsk Mc-Kinney Møller Institute University of Southern Denmark Odense Denmark

ISBN: (纸本)9789897587061

Digital Twins have revolutionized the domain of Modeling and Simulation by making use of the growing and cost-efficient possibilities to extract data from systems, as well as the increasing computational power. At the same time, Digital Twins have enabled tremendous advances in diverse cyber-physical systems by enabling better monitoring, predictive maintenance, design optimization, and informed decision-making. As their popularity evolved, the understanding of what a Digital Twins has become more and more dispersed and unclear. Here, we offer understanding of what a Digital Twin is based on our experience in research within its native domain of Modeling and Simulation, with a concrete focus on the key considerations that need to be made when developing Digital Twins or working with them. We, furthermore, emphasize the need to include all available knowledge for better-informed Digital Twins. To illustrate our ideas and vision, we use case studies from our research. © 2024 by SCITEPRESS – Science and technology Publications, Lda.

关键词： Digital Twins Expert Knowledge Fusion of Data Goal-Oriented Key Considerations Simulation

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"It’s like an explosion": Cyberwarfare harms for civilian population in Ukraine during the Russian invasion

"It’s like an explosion": Cyberwarfare harms for civilian p...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Kulyk, Oksana Kickbusch, Jari Mayer, Peter IT University of Copenhagen Copenhagen Denmark Department of Mathematics and Computer Science University of Southern Denmark Odense Denmark Institute of Applied Informatics and Formal Description Methods Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9798400713958

Studies on civilian costs of cyberwarfare operations are crucial in understanding how to protect the population in large-scale cyberattacks conducted by state actors. In this preliminary study, we conduct interviews (N=5) focusing on the harms experienced by Ukrainian civilians due to Russian cyberwarfare operations since the start of the full-scale invasion in 2022 as well as mitigation measures for protection of the civil society. The findings include harms such as lack of information, lack of access to essential services, physical harms, anxiety and lack of trust in the government. The study shows that cyberwarfare operations have the potential of causing severe harm to civilians and that there is a need for future studies to be conducted and adopted by institutions in order to develop measures to mitigate, not only in Ukraine but in any countries in risk of such attacks. © 2025 Copyright held by the owner/author(s).

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A Conceptual Framework for Digital Twins of Multi-Agent Systems 16

A Conceptual Framework for Digital Twins of Multi-Agent Syst...

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16th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Lee, Hui Min Jamali, Ruhollah Lazarova-Molnar, Sanja Institute of Applied Informatics and Formal Description Methods Karlsruhe Institute of Technology Kaiserstr. 89 Karlsruhe76133 Germany The Maersk Mc Kinney Moller Institute University of Southern Denmark Campusvej 55 Odense5230 Denmark

Multi-agent Systems (MASs) are complex systems made up of agents that can be any entities with the ability to interact autonomously and make decentralized decision-making to solve complex problems. Data-driven Agent-based Modeling and Simulation (DDABMS) equips MASs with access to decisions based on near-real-time data, allowing for more informed decisions for systems' enhancements. Digital Twins (DTs) can further enhance MASs by serving as virtual replicas that enable what-if scenarios exploration and allow continuous validation and refinement of the underlying models with real-time data from MASs. However, we discovered a gap in systematically integrating DTs with DDABMS, as existing efforts focus on specific problems and domains rather than providing a generalized framework to develop DTs with DDABMS. This paper addresses this gap by proposing a generalized framework to develop DTs for MASs with DDABMS. To demonstrate the practicability of our proposed framework for modeling and simulation of complex systems, we present an illustrative case study based on an epidemiological Susceptible-Infected-Recovered model. © 2025 Elsevier B.V.. All rights reserved.

关键词： Multi agent systems

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Challenges in Developing Digital Twins for Labor-intensive Manufacturing Systems: A Step towards Human-centricity 15

Challenges in Developing Digital Twins for Labor-intensive M...

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15th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2024 / The 7th International Conference on Emerging Data and Industry 4.0, EDI40 2024

作者： Götz, Manuel Lazarova-Molnar, Sanja Institute of Applied Informatics and Formal Description Methods Karlsruhe Institute of Technology Kaiserstr 89 Karlsruhe76133 Germany Mcersk Mc-Kinney Moller Institute University of Southern Denmark Campusvej 55 Odense5230 Denmark

Many existing manufacturing systems still rely heavily on human workers as the backbone of their production processes. Such systems are commonly termed labor-intensive. Developing Digital Twins for labor-intensive manufacturing lines is a complex and challenging task as human involvement adds another level of uncertainty. While Digital Twins offer numerous benefits, such as improved efficiency, reduced downtime, and enhanced decision-making, they also come with unique challenges when they need to be developed for labor-intensive manufacturing systems. In this paper, we discuss the main challenges and their implications that arise from existing research. Considering these challenges, we propose a framework for developing data-driven Digital Twins of labor-intensive manufacturing systems as an initial step towards addressing these challenges. We illustrate the challenges associated with Digital Twins of labor-intensive manufacturing systems through a practical case study derived from our collaboration with two companies. In the case study, we make necessary preparations for developing Digital Twins for decision support in job scheduling in a hybrid machine-worker environment while considering the well-being of workers. © 2024 Elsevier B.V.. All rights reserved.

关键词： Decision making

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Towards Fusing Data and Expert Knowledge for Better-Informed Digital Twins: An Initial Framework 15

Towards Fusing Data and Expert Knowledge for Better-Informed...

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15th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2024 / The 7th International Conference on Emerging Data and Industry 4.0, EDI40 2024

作者： Jungmann, Michelle Lazarova-Molnar, Sanja Institute of Applied Informatics and Formal Description Methods Karlsruhe Institute of Technology Kaiserstr 89 Karlsruhe76133 Germany Mcersk Mc-Kinney Moller Institute University of Southern Denmark Campusvej 55 Odense5230 Denmark

Data-driven Digital Twin approaches have gained popularity in research literature, particularly in relation to Industry 4.0 achievements. Data-driven Digital Twins process widely available real-time data in an automated manner to extract models from various systems of interest. However, these model extraction approaches commonly lack systematic integration of expert knowledge from human experts, such as engineers. Combining valuable expert knowledge with data-driven model extraction approaches is a highly complex task that can significantly contribute to more accurate models within shorter time periods. In this paper, we provide a comprehensive overview of the research done on fusion of data and expert knowledge for Digital Twins to identify and describe the current gap. Resulting from our findings, we propose an initial framework for integrating expert knowledge and data for Digital Twin model extraction. Subsequently, we provide an overview of the main challenges and opportunities in fusing data and expert knowledge in the context of Digital Twins. © 2024 Elsevier B.V.. All rights reserved.

关键词： Industry 4.0

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Named Entity Recognition for digitised archival documents in German 24

Named Entity Recognition for digitised archival documents in...

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Joint of Posters, Demos, Workshops, and Tutorials of the 24th International Conference on Knowledge Engineering and Knowledge Management, EKAW-PDWT 2024

作者： Garay, Nele Vafaie, Mahsa Sack, Harald FIZ Karlsruhe - Leibniz Institute for Information Infrastructure Germany Applied Informatics and Formal Description Methods (AIFB) Karlsruhe Institute of Technology (KIT) Germany

This paper presents an experiment that evaluates the effectiveness of two different Named Entity Recognition (NER) tools at extracting entities directly from the output of an Optical Character Recognition (OCR) workflow. The authors initially developed a test dataset comprising both raw and corrected OCR outputs, which were manually annotated with tags for Person, Location, and Organisation. Subsequently, they applied each NER tool to both the raw and corrected OCR outputs, evaluating their performance by comparing the precision, recall, and F1 scores against the manually annotated data. © 2024 Copyright for this paper by its authors.

关键词： Digital Cultural Heritage Named Entity Recognition (NER) Optical Character Recognition (OCR)

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The landscape of ontologies in materials science and engineering: a survey and evaluation 1

The landscape of ontologies in materials science and enginee...

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1st International Workshop on Semantic Materials Science: Harnessing the Power of Semantic Web Technologies in Materials Science, SeMatS 2024

作者： Norouzi, Ebrahim Waitelonis, Jörg Sack, Harald FIZ Karlsruhe - Leibniz-Institute for Information Infrastructure Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany Karlsruhe Institute of Technology Institute of Applied Informatics and Formal Description Methods Kaiserstrasse 89 Karlsruhe76133 Germany

Ontologies are widely used in materials science to describe experiments, processes, material properties, and experimental and computational workflows. Numerous online platforms are available for accessing and sharing ontologies in Materials Science and Engineering (MSE). Additionally, several surveys of these ontologies have been conducted. However, these studies often lack comprehensive analysis and quality control metrics. This paper provides an overview of ontologies used in Materials Science and Engineering to assist domain experts in selecting the most suitable ontology for a given purpose. Sixty selected ontologies are analyzed and compared based on the requirements outlined in this paper. Statistical data on ontology reuse and key metrics are also presented. The evaluation results provide valuable insights into the strengths and weaknesses of the investigated MSE ontologies. This enables domain experts to select suitable ontologies and to incorporate relevant terms from existing resources. © 2024 CEUR-WS. All rights reserved.

关键词： Knowledge representation Materials informatics Ontology evaluation Ontology reuse

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Essential Data Requirements for Industrial Energy Efficiency with Digital Twins: A Case Study Analysis 15

Essential Data Requirements for Industrial Energy Efficiency...

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15th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2024 / The 7th International Conference on Emerging Data and Industry 4.0, EDI40 2024

作者： Khodadadi, Atieh Lazarova-Molnar, Sanja Institute of Applied Informatics and Formal Description Methods Karlsruhe Institute of Technology Kaiserstr. 89 Karlsruhe76133 Germany The Maersk Mc-Kinney Moller Institute University of Southern Denmark Campusvej 55 Odense5230 Denmark

The rapid expansion of industrialization and the increase in energy demand make energy resource management necessary in various industries. Creating digital twins from available streaming data offers a practical way to enhance the efficiency of manufacturing systems. Digital twins allow for detailed analysis of key metrics, such as the energy consumption of complete manufacturing systems. Utilizing digital twins can greatly assist in improving manufacturing processes for reduced energy consumption. In this paper, we investigate the data requirements for the generation of energy-oriented digital twins of manufacturing systems. For this purpose, we developed a case study to illustrate and outline the necessary data requirements for a manufacturing system to enable the collection of data that can be used to extract energy-oriented simulation models. We, subsequently, analyze and generalize our findings that can be applied to a broader context. © 2024 Elsevier B.V.. All rights reserved.

关键词： Energy efficiency

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Making Sense of Projects—Developing Project Portfolio Management Capabilities

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Schmalenbach Journal of Business Research 2024年第2期76卷 293-325页

作者： Ahlemann, Frederik Bergan, Peder Karger, Erik Greulich, Malte Reining, Stefan Chair of Information Systems and Strategic IT Management University of Duisburg-Essen Universitätsstr. 12 Essen 45141 Germany Department of Economics and Management Institute of Applied Informatics and Formal Description Methods (AIFB) Karlsruhe Institute of Technology Karlsruhe Germany

Project management and project portfolio management (PPM) foster competitiveness by facilitating the implementation of organizational strategy. Although organizations often struggle to develop PPM capabilities, the academic community does not have an in-depth understanding of the conditions for successfully developing these capabilities. In response, we conducted a multiple-case study with 50 interviewees to develop a theoretical model of the PPM capability-building process. This model is built on the notion of organizational sensemaking and identifies aspects that comprehensively explain why it usually takes so long to develop PPM capabilities. We conceptualize the PPM capability-building process as one that is strongly influenced by (1) the effects of structural rearrangements, (2) the appropriate use of external resources during that process, (3) the role of executive support and legitimization, (4) episodes of regression, and (5) the need for internalization and habitualization. In addition, we provide starting points for explaining organizational capability building in more general terms. © The Author(s) 2024. corrected publication 2024.

关键词： Capabilities Capability building External resources Habitualization Internalization Interpretive schema Legitimization M10 M14 M15 O31 O32 O33 Organizational sensemaking Project management Project portfolio management

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Surrogate Modeling: Review and Opportunities for Expert Knowledge Integration 16

Surrogate Modeling: Review and Opportunities for Expert Know...

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16th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Šturek, Dušan Lazarova-Molnar, Sanja Institute of Applied Informatics and Formal Description Methods Karlsruhe Institute of Technology Kaiserstr. 89 Karlsruhe76133 Germany Mærsk Mc-Kinney Møller Institute University of Southern Denmark Campusvej 55 Odense5230 Denmark

Digital Twins (DTs), essential for digitalization and Industry 4.0, demand high-fidelity yet computationally efficient models for real-time replication of physical systems. However, simulating continuous dynamical systems, often stiff and highly granular, poses significant computational challenges. Driven by industry's need for more efficient simulations, this study explores Surrogate Modeling (SMing) as a solution to reduce computational costs while preserving accuracy, enabling real-time performance, iterative design optimization and enhancing feasibility of DT simulations. We review existing traditional and machine learning SMing approaches, analyzing their limitations, accuracy and efficiency. We, furthermore, emphasize the critical role of domain expertise in SMing workflows and explore systematic strategies for incorporating expert knowledge to improve model reliability and applicability. Finally, we identify and discuss challenges and opportunities that emerge by the fusion of expert knowledge with machine learning techniques, highlighting their potential to advance next-generation SMing techniques. © 2025 Elsevier B.V.. All rights reserved.

关键词： High-fidelity Simulation Knowledge Integration Surrogate Modeling

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