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Enabling Data-Driven Nursing Innovations: User-centered Development of a Nursing Data Module

Current Directions in Biomedical engineering 2023年第1期9卷 339-342页

作者： Warnecke, Maren Holle, Daniela Burmann, Anja Fraunhofer Institute for Software and Systems Engineering ISST Speicherstrasse 6 Dortmund Germany HS Gesundheit Bochum Bochum Germany Fraunhofer Institute for Software and Systems Engineering ISST Dortmund Germany

The documentation landscape for nursing care data in Germany is predominantly heterogeneous and unstructured. Therefore, insightful methods such as Artificial Intelligence (AI) are difficult to implement. We propose a stepwiseapproach that identifies relevant information from nursing theory and practice and maps it to a standardized nursing core data set to enable data-based improvement of nursing care. This can be used for various use-cases in care, such as risk detection and prevention in diverse care contexts. Many care processes can benefit of a cross-facility and standardized repository and associated applications. We propose an approach that enables the use of AI while leveraging consensus and evidenced-based expert knowledge from nursing science. © 2023 the author(s), published by Walter de Gruyter Berlin/Boston.

关键词： Nursing

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Design Features for Data Trustee Selection in Data Spaces 13

Design Features for Data Trustee Selection in Data Spaces

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13th International Conference on Data Science, Technology and Applications, DATA 2024

作者： Steinert, Michael Tebernum, Daniel Hupperz, Marius Fraunhofer Institute for Software and Systems Engineering ISST Dortmund Germany

ISBN: (纸本)9789897587078

As the world becomes increasingly digital, data is becoming a critical resource. When used effectively, it can lead to more accurate forecasts, process optimization, and the creation of innovative business models. The necessary data is often distributed across multiple organizations, and its full value can only be realized through shared collaboration. Data spaces provide organizations with a platform for sovereign and secure data sharing. To enable legally secure data sharing and ensure compliance with regulations, data trustees play a critical role as trusted intermediaries. However, choosing a suitable data trustee that meets the needs of the participants who want to share data with each other is difficult. Our study seeks to elucidate the process by which participants of a data space can choose an appropriate data trustee. To this purpose, we have implemented a whitelist approach. We report on the results of our design science research project, which includes design features to facilitate the integration of our whitelist approach into different data space instantiations. Potential shortcomings were identified and addressed during an expert workshop. By providing verified design knowledge, we help practitioners in the data space community to incorporate the concept of how to select the most appropriate data trustee. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Data consistency

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Reliable and Content-specific Support for Keyword Selection through AI and Statistics Characterising Educational Content with Large Language Models & Agreement Analyses

Reliable and Content-specific Support for Keyword Selection ...

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作者： Strube, Tom Nowak, Tom Pokotylo, Mariia Kuhlenkötter, Bernd Fraunhofer Institute for Software and Systems Engineering Dortmund Germany Ruhr University Bochum Bochum Germany

Due to the recent popularity and availability of Large Language Models (LLMs), creators of educational materials can more efficiently extract keywords for use in personalised learning recommendations than ever before. However, due to the LLMs' probabalistic nature, the automation of the otherwise labour-intense keyword extraction inherits the risk of biased and non-explainable results. In this research, we present an original framework to enhance keyword selection based on content title and description through a novel, reliability-sensitive, keyword selection algorithm. For this, we collected 38 potential keywords (together with their definitions) for five topics on dementia care from previous studies, together with two contents per topic. To assess the new method's support in extracting keywords, we then prompted 5 human experts and 3 LLMs (using Retrieval Augmented Generation (RAG) for the keyword definitions) to select keywords to include and exclude for each content. Using Krippendorf's a metric, we then were able to adapt to the present agreement, and to reliably select keyword sets for inclusion and exclusion for each content individually. Last, we compared these LLM-based keyword sets with those selected by humans to assess the impact of the adaptive keyword selection algorithm. Overall, the results suggest that LLMs generally struggle with the task (66% of extraction attempts either contained hallucinated or did not return any keywords), and topic-wise internal agreement is low (a=0.59 (0.42) for model 3 (using RAG) on average;α=0.68 for human raters). Due to this, the reliable keyword selection resulted in a median set of 6|27 keywords for inclusion|exclusion per topic, with many of those keywords being within the benchmark keyword sets selected by human raters. To conclude, this approach shows effective in adapting to different levels of agreement in extracting keywords. © 2024 by Walter de Gruyter Berlin/Boston.

关键词： Reliability analysis

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Enabling Data-Driven Nursing Innovations: User-centered Development of a Nursing Data Module

Enabling Data-Driven Nursing Innovations: User-centered Deve...

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关键词： Nursing

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Extended Abstract of Poster: STARS: Tree-Based Classification and Testing of Feature Combinations in the Automated Robotic Domain 18

Extended Abstract of Poster: STARS: Tree-Based Classificatio...

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18th IEEE International Conference on software Testing, Verification and Validation Workshops, ICSTW 2025

作者： Schallau, Till Schmid, Dominik Pawlinorz, Nick Naujokat, Stefan Howar, Falk TU Dortmund University Dortmund44227 Germany Fraunhofer Institute for Software and Systems Engineering Dortmund44147 Germany

ISBN: (纸本)9798331534677

Testing complex systems is crucial for ensuring safety, especially in automated driving, where diverse data sources and variable environments pose challenges. Here, robust safety validation is critical but exhaustive n-way combinatorial testing is impractical due to the vast number of test cases. The STARS framework uses tree-based scenario classifiers to limit feature combinations in a given domain. © 2025 IEEE.

关键词： Automation

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Supporting Changes in Digital Ownership and Data Sovereignty Across the Automotive Value Chain with Catena-X 6

Supporting Changes in Digital Ownership and Data Sovereignty...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Manoury, Marvin Riedelsheimer, Theresa Hellmeier, Malte Meyer, Tom Fraunhofer Institute for Production Systems and Design Technology IPK Pascalstrasse 8-9 Berlin10587 Germany Fraunhofer Institute for Software and Systems Engineering ISST Speicherstraße 6 Dortmund44147 Germany

Considering the concept of a Circular Economy, which entails several life cycles of, e.g., vehicles, their components, and materials, it is important to investigate how the respective Digital Twins are managed over the lifecycle of their physical assets. This publication presents and compares three approaches for managing Digital Twins in industrial use cases. For the aggregation of these approaches, a user-centered design approach has been used in the research project Catena-X which focuses on the automotive industry. The results are analyzed qualitatively based on aspects such as data ownership and data sovereignty. It was identified, that all three approaches have limitations and benefits which make them useful in different scenarios. An overview of their strengths and weaknesses is presented to select an appropriate approach In similar setups. The results described in this publication can be used to select the best update approach in data spaces where multiple stakeholders are able to update information on a single asset. © 2025 The Authors.

关键词： Supply chains

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Data sovereignty needs in agricultural use cases 43

Data sovereignty needs in agricultural use cases

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43. Jahrestagung der Gesellschaft fur Informatik in der Land-, Forst- und Ernahrungswirtschaft - Resiliente Agri-Food-Systeme: Herausforderungen und Losungsansatze, GIL 2023 - 43rd Annual Conference of the Society for Informatics in Agriculture, Forestry, and Food Industry - Resilient Agri-Food systems: Challenges and Solutions, GIL 2023

作者： Boye, Fredrik Matar, Raghad Neuschwander, Philipp Fraunhofer IVI Fraunhofer Institute for Transportation and Infrastructure Systems Dresden01069 Germany Fraunhofer IESE Fraunhofer Institute for Experimental Software Engineering Kaiserslautern67663 Germany

ISBN: (纸本)9783885797241

With today's shift towards digital farming, data transfer and processing are becoming increasingly essential for optimizing farm operations. In the complex arable farming sector, there exist numerous use cases involving a variety of actors and processes. Designing new digital ecosystems, such as agricultural data spaces that support existing workflows while enabling new opportunities for data-driven services, requires a good understanding of existing processes and data sovereignty needs. In this paper, we categorize data exchange use cases in arable farming and analyze the respective data sovereignty needs for each category. The results of our contribution can be used as a basis for further analysis and evaluation of data-sharing approaches in terms of their suitability for meeting different data sovereignty needs in agriculture, as well as in the process of requirements analysis when designing such systems. © 2023 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Electronic data interchange

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Evaluation Methods and Replicability of software Architecture Research Objects

Evaluation Methods and Replicability of Software Architectur...

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2023 Fachtagung des GI-Fachbereichs softwaretechnik, software engineering 2023 - 2023 Conference of the GI software engineering Division, software engineering 2023

作者： Konersmann, Marco Kaplan, Angelika Kühn, Thomas Heinrich, Robert Koziolek, Anne Reussner, Ralf Jürjens, Jan al-Doori, Mahmood Boltz, Nicolas Ehl, Marco Fuchß, Dominik Großer, Katharina Hahner, Sebastian Keim, Jan Lohr, Matthias Sağlam, Timur Schulz, Sophie Töberg, Jan-Philipp Software Engineering RWTH Aachen University Germany Karlsruhe Institute of Technology Germany University of Koblenz-Landau Germany Fraunhofer Institute for Software and Systems Engineering ISST Germany

ISBN: (纸本)9783885797265

Our paper at the 19th IEEE International Conference on software Architecture (ICSA 2022) started by noticing that software Architecture (SA) as research area experienced an increase in empirical research. Empirical research builds a sound foundation for validity and comparability. A current overview of the evaluation and replicability of SA research objects could help to discuss our empirical standards as a community. However, no current overview existed. We assessed the current state of practice of evaluating SA research objects and replication artifact provision in full technical conference papers from ICSA and the European Conference on software Architecture (ECSA) 2017-2021. We first developed a categorization schema for SA research object evaluation and artifact provisioning. In a systematic literature review with 153 papers, we then classified the papers according to that schema. From our findings we derive and describe four proposals for improving the state of practice in evaluating SA research objects. © 2023 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： software architecture

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POTENTIALS AND BARRIERS OF THE METAVERSE FOR CIRCULAR ECONOMY

POTENTIALS AND BARRIERS OF THE METAVERSE FOR CIRCULAR ECONOM...

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2024 Winter Simulation Conference, WSC 2024

作者： Kunert, Julia van der Valk, Hendrik Scheerer, Hannah Hoppe, Christoph TU Dortmund University NRW Dortmund Germany Fraunhofer Institute for Software and Systems Engineering ISST NRW Dortmund Germany Dept. of Industrial Manufacturing Fraunhofer ISST NRW Dortmund Germany

ISBN: (纸本)9798331534202

Sustainability is a challenge for society that circular economy tries to tackle. The metaverse, as an emerging technology that incorporates digital twins and simulation in an immersive virtual environment, has not been thoroughly investigated in connection to circular economy. Thus, the purpose of this study is to summarize the potentials and barriers of the use of the metaverse for circular economy. By conducting a structured literature review, this paper categorizes the findings into dimensions that are important for both the metaverse and circular economy. A variety of potentials and barriers that cover different perspectives important for businesses aiming to comply with circular economy principles is discovered. The findings include potentials and barriers in several areas, like the access to the metaverse, connected costs, data, knowledge transfer, collaboration, innovation, product design, production planning, training of employees, and transportation. The results can be used to promote the implementation of circular economy principles. © 2024 IEEE.

关键词： Virtual environments

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COMBINING SIMULATION AND RECURRENT NEURAL NETWORKS FOR MODEL-BASED CONDITION MONITORING OF MACHINES

COMBINING SIMULATION AND RECURRENT NEURAL NETWORKS FOR MODEL...

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2024 Winter Simulation Conference, WSC 2024

作者： Wuttke, Alexander Rabe, Markus Hunker, Joachim Diepenbrock, Jan-Philipp Dept. IT in Production and Logistics TU Dortmund University Dortmund Germany Fraunhofer Institute for Software and Systems Engineering Dortmund Germany IVA Schmetz GmbH Menden Germany

ISBN: (纸本)9798331534202

Maintenance is pivotal in industry, with condition-based maintenance emerging as a key strategy. This involves monitoring the machine condition through sensor data analysis. Model-based approaches compare observed data with expected values from models, which requires high-quality models. An established method is to use simulation models, which in many cases produce good results but may lack precision due to uncertainties. Alternatively, models created by machine learning can detect patterns directly from data. This paper proposes combining simulation models with machine learning models, leveraging the simulation’s a-priori knowledge and machine learning’s data patterns to enhance models for condition monitoring. Recurrent neural networks are suggested as the machine learning method. The paper outlines a systematic approach and demonstrates its application in an industrial use case, which investigates vacuum processes in industrial furnaces. © 2024 IEEE.

关键词： Neural network models

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