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Modellierung 2024 (LNI P348)

https://dl.gi.de/handle/20.500.12116/43784
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Auflistung Modellierung 2024 (LNI P348) nach Titel

1 - 10 von 23
  • Konferenzbeitrag
    Accessibility in Conceptual Modeling Research and Tools
    (Modellierung 2024, 2024) Sarioglu, Aylin; Metin, Haydar; Bork, Dominik
    The reports on Disability by the World Health Organization show that the number of people with disabilities is increasing. Consequently, accessibility should play an essential role in information systems engineering research. While software and web engineering research acknowledge this need by providing, e.g., web accessibility guidelines and testing frameworks, we show in this paper, based on a systematic review of the literature and current modeling tools, that accessibility is, so far, a blind spot in conceptual modeling research. With the paper at hand, we aim to identify current research gaps and delineate a vision toward more inclusive, i.e., disability-aware conceptual modeling. One key finding relates to a gap in research and tool support concerning physical disabilities. Based on these results, we further present the first modeling tool that can be used keyboard-only, thereby including users with physical disabilities to engage in conceptual modeling.
  • Konferenzbeitrag
    Advancing Virtual Coaching in Healthcare: Towards A Unified Terminology and Reference Model
    (Modellierung 2024, 2024) Gißke, Carola; Weiman, Thure Georg; Schlieter, Hannes
    Virtual coaching applications, designed to facilitate behavior change through adaptive coaching activities, hold promise for personalized interventions, particularly in healthcare. While existing literature explores various aspects of virtual coaches (VCs), there is a lack of comprehensive conceptual analysis, and inconsistent terminology further complicates their understanding. The present paper aims to demonstrate the ongoing work on systematically categorizing and describing the components of VCs and, thereby, creating a reference model reusable for different contexts. Based on a systematic literature review, concepts related to VC interventions will be derived, categorized, and linked to each other, forming a unified framework that could simplify the process of designing VCs and provide the foundation for dedicated building tools in terms of low/no-code platforms. Moreover, the work contributes with a consolidated terminology, and the proposed model may also be used to identify underexplored research areas, enhancing research opportunities in the field.
  • Konferenzbeitrag
    Applicability of Model Checking for Verifying Spacecraft Operational Designs
    (Modellierung 2024, 2024) Chrszon, Philipp; Maurer, Paulina; Saleip, George; Müller, Sascha; Fischer, Philipp M. ; Gerndt, Andreas; Felderer, Michael
    This is a summary of the paper Applicability of Model Checking for Verifying Spacecraft Operational Designs which has been published at the 26th International Conference on Model Driven Engineering Languages and Systems (MODELS 23).
  • Konferenzbeitrag
    Automatisierte Generierung fachlicher Prozessmodelle basierend auf natürlichsprachlichen Prozessbeschreibungen
    (Modellierung 2024, 2024) von Olberg, Pauline; Strey, Lukas
    Die manuelle Erstellung von Prozessmodellen ist eine gängige Tätigkeit im Rahmen der Softwareentwicklung. Die Erstellung der Modelle stellt allerdings eine zeitintensive Aufgabe für IT-Fachkräfte dar. Mit dem Ziel, die Fachkräfte zu entlasten, stellen wir die Methode NL2BPMN und einen Prototyp vor, durch welche natürlichsprachliche sowie fachspezifische Prozessbeschreibungen automatisiert in BPMN-Prozessmodelle transformiert werden können. Die Methode basiert auf Natural Language Processing (NLP) und bedient sich unter anderem dem Part-of-Speech-Tagging sowie dem Dependency Parsing. Ein Bestandteil der Methode ist die Verwendung einer Fachbegriffe-Liste als zusätzlicher Input neben Prozessbeschreibungen, um Fachbegriffe, die aus mehreren Wörtern bestehen, als zusammengehörige Begriffe zu verarbeiten. Ein Vergleich von automatisiert generierten Modellen mit manuell erstellten Modellen zeigt Erfolgsquoten von über 90 \% in allen Bewertungskategorien, sofern eine Fachbegriffe-Liste verwendet wird.
  • Konferenzbeitrag
    Beobachtungen und Einsichten zu Repositorys von BPMN-Modellen
    (Modellierung 2024, 2024) Laue, Ralf; Läuter, Martin
    Bei der empirischen Untersuchung der Praxis der Geschäftsprozessmodellierung ist man auf eine umfangreiche, vielfältige und gleichzeitig zur Aufgabenstellung passende Datenbasis angewiesen. Wir untersuchen eine Reihe öffentlich zugänglicher Modellrepositorys mit BPMN-Modellen, die in den vergangenen Jahren entstanden sind. Wir weisen auf Eigenarten der Repositorys hin, die die Verarbeitung der Daten erschweren und die Datenqualität beeinträchtigen. Besonders diskutiert wird das in bisherigen Arbeiten nicht betrachtete Phänomen von de facto inhaltsgleichen Modellen in bei bitweisem Vergleich verschiedenen Dateien. Wir diskutieren die Auswirkung solcher Duplikate und schlagen eine der jeweiligen Aufgabenstellung angepasste Filterung vor. Wir begründen, warum dieses Vorgehen insbesondere bei Ansätzen zum maschinellen Lernen beachtet werden sollte. Wir stellen fest, dass die empfohlenen Maßnahmen zur Sicherung der Datenqualität in aktuellen Veröffentlichungen häufig noch nicht beachtet werden, was die Aussagekraft von deren Ergebnissen in Frage stellen kann.
  • Konferenzbeitrag
    Digital Transformation through Conceptual Modeling: The NEMO Summer School Use Case
    (Modellierung 2024, 2024) Völz, Alexander; Vaidian, Iulia
    In the digital age, achieving a balance between human creative thinking and technology capabilities is crucial. Recognizing the potential of such collaborations, OMiLAB (Open Model Initiative Laboratory) developed a conceptual framework for establishing experimental innovation spaces in which skills to advance human-machine interaction can be taught and applied. The resulting Digital Innovation Environment incorporates both business and engineering perspectives, emphasizing the importance of interdisciplinary settings. Conceptual models and Digital Twins play a pivotal role within the environment, seamlessly bridging business strategies with cyber-physical systems. This paper offers a comprehensive understanding of the OMiLAB network, highlighting its alignment with the principles of a Community of Practice and emphasizing the knowledge exchange, exemplified by the NEMO Summer School Series. We present insights, best practices, and educational paradigms vital for navigating the digital transformation landscape.
  • Konferenzbeitrag
    Exploring Conceptual Data Modeling Processes: Insights from Clustering and Visualizing Modeling Sequences
    (Modellierung 2024, 2024) Winkler, Philip; Rosenthal, Kristina; Strecker, Stefan
    Research on performing conceptual data modeling finds conceptual modelers to exhibit distinct procedural patterns of data modeling: for example, when performing a data modeling task applying the Entity-Relationship Model, a repeatedly observed pattern refers to first modeling entity types, attributes and their data types, then relationship types and their cardinalities in a subsequent step. To identify patterns in data modeling processes, we cluster and visualize sequences of modeling activities of 22 conceptual data modelers at different levels of data modeling expertise. In particular, we process modeler-tool interactions in a browser-based modeling tool to visualize sequences regarding the specific modeling activity of adding entity types, attributes and relationship types to a data model, and use hierarchical clustering to identify procedural patterns based on their similarity. We find procedural patterns to follow a distinct top-down and sequential way of proceeding and identify modeling sequences with a separate phase for modeling relationship types. Our findings prepare for designing tailored modeler tool support and inform instructors and learners on the process of conceptual data modeling.
  • Konferenzbeitrag
    A flexible operation-based infrastructure for collaborative model-driven engineering
    (Modellierung 2024, 2024) Herac, Edvin; Marchezan, Luciano; Assunção, Wesley; Haas, Rainer; Egyed, Alexander
    Current engineering practices to create complex systems rely on highly interdisciplinary teams, potentially globally distributed, working with heterogeneous artifacts. For instance, in a robotics project, collaboration from multiple engineers across different domains such as mechanical, electronic, and software is required. However, achieving proper collaboration to correctly and efficiently develop complex systems is not a trivial activity. The artifacts developed in each domain, usually represented as models, use different structures (e.g., metamodels) and are managed in different tools, but somehow related to each other.
  • Konferenzbeitrag
    From Natural Language to Web Applications: Using Large Language Models for Model-Driven Software Engineering
    (Modellierung 2024, 2024) Netz, Lukas; Michael, Judith; Rumpe, Bernhard
    We evaluate the usage of Large Language Models (LLMs) to transform natural language into models of a predefined domain-specific language within the context of model-driven software engineering. In this work we test systematically the reliability and correctness of the developed tooling, to ensure its usability in an automated model-driven engineering context. Up to now, LLMs such as ChatGPT were not sophisticated enough to yield promising results. The new API-Access and the release of GPT-4, enabled us to develop improved tooling that can be evaluated systematically. This paper introduces an approach that can produce a running web application based on simple informal specifications, that is provided by a domain expert with no prior knowledge of any DSL. We extended our toolchain to include ChatGPT and provided the AI with additional DSL-specific contexts in order to receive models that can be further processed. We performed tests to ensure the semantic and syntactic correctness of the created models. This approach shows the potential of LLMs to successfully bridge the gap between domain experts and developers and discusses its current limitations.
  • Konferenzbeitrag
    Maximizing Reuse and Interoperability in Industry 4.0 with a Minimal Data Exchange Format for Machine Data
    (Modellierung 2024, 2024) Tacke Genannt Unterberg, Leah; Koren, István; van der Aalst, Wil M.P.
    Data interoperability in Industry 4.0 is a continuous challenge for industry and research. Many organizations face the challenge of managing data lakes that, without proper governance, risk becoming disorganized `data swamps' with disparate data models and formats. This heterogeneity leads to inefficient data utilization.Standardization efforts have produced suites of extensive models as they try to accommodate diverse requirements while still being comprehensive. Their complexity has hindered their adoption. To address this, we propose a minimal intermediate meta model for a frequently considered type of data in smart manufacturing, namely Machine Data. This type of data is central to industrial IoT platforms and research efforts on Digital Shadows & Twins. It encompasses raw time series and event data from sensors and digital controllers. This model-in-the-middle is intended to bridge the gap between heterogeneous source systems and highly structured and semantically clean input for data science techniques. To be broadly applicable, it has to be minimal and favor abstraction over details. We equip it with a standardized exchange format based on CSV, which reduces friction in data sharing. Furthermore, we provide a precise mathematical formalization that connects it to the language of data science methods. This enables the generic implementation of methods that can easily be reused and combined. Finally, we validate the model together with initial tool support in the large-scale cluster of excellence Internet of Production (IoP). We conclude that it is possible and feasible to accelerate the realization of the ambitions for the future of manufacturing using such minimal models.