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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 Erscheinungsdatum

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  • 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
    Transforming Object-Centric Process Models into BPMN 2.0 Models in the PHILharmonicFlows Framework
    (Modellierung 2024, 2024) Breitmayer, Marius; Arnold, Lisa; Pejic, Marko; Reichert, Manfred
    Business processes can be modeled using a plethora of different paradigms including activity-centric (e.g., imperative, declarative), and data-centric processes. The former focus on the process activities to be executed as well as their execution order and constraints, whereas the latter deal with the data required to progress during process execution. Both representations, however, allow describing the same process, but from different viewpoints. Consequently, a transformation between process representations based on the different paradigms yields promising perspectives for enabling a holistic view on both the behavior and the data perspective of a process and fosters a common understanding of different paradigms. This paper presents an approach for transforming object-centric processes (i.e., object lifecycle processes and their interactions) into corresponding activity-centric representations modeled in terms of BPMN 2.0. We present seven transformation rules for mapping an object- to an activity-centric process, illustrated along a running example. We evaluate the approach based on a proof-of-concept implementation that can automatically perform the necessary transformations and has been applied in multiple scenarios. Overall, our approach for transforming object-centric processes into BPMN 2.0 models provides new insights into the relationship between the two paradigms and enables a more flexible and effective way of modeling business processes in general.
  • Konferenzbeitrag
    The Triptych Paradigm Revisited
    (Modellierung 2024, 2024) C. Mayr, Heinrich; Thalheim, Bernhard
    Modeling is crucial to most disciplines. It has been widely researched, taught, and practiced for many years. One should think that with all the intelligent works, published in thousands of papers, hardly any fundamental question remains open.However, even a simple sounding question like 'When is a model a conceptual one?' has no universally valid or at least broadly accepted answer. Given the many modeling languages and tools offered as ``conceptual'', such question may seem quixotic and academic. However, a scientist should be concerned if he or she researches and teaches in the field of modeling but cannot give an incontrovertible answer. We report here extensions of the work that should facilitate answering basic questions of this kind. It is based on a taxonomy of the characteristics of (conceptual) models and the ``triptych paradigm'' assuming that conceptual modeling consists of a central model part, the instrumentation by a language, and the inherited world semantics within the given application. This leads to the triptych dimensions of models, their linguistic representation, and the meaning and semantic grounding of each intensional model element. It also allows a systematic view of the hierarchy-creating relationships occurring in each dimension.
  • Konferenzbeitrag
    Process Mining for Unstructured Data: Challenges and Research Directions
    (Modellierung 2024, 2024) Koschmider, Agnes; Aleknonytė-Resch, Milda; Fonger, Frederik; Imenkamp, Christian; Lepsien, Arvid; Apaydin, Kaan; Janssen, Dominik; Langhammer, Dominic; Ziolkowski, Tobias; Zisgen, Yorck
    The application of process mining for unstructured data might significantly elevate novel insights into disciplines where unstructured data is a common data format. To efficiently analyze unstructured data by process mining and to convey confidence into the analysis result, requires bridging multiple challenges. The purpose of this paper is to discuss these challenges, present initial solutions and describe future research directions. We hope that this article lays the foundations for future collaboration on this topic.
  • Konferenzbeitrag
    Modeling difficulties in creating conceptual data models: Multimodal studies on individual modeling processes
    (Modellierung 2024, 2024) Rosenthal, Kristina; Strecker, Stefan; Snoeck, Monique
    Combining complementary modes of observation of modelers' modeling processes, we study modeling difficulties encountered by modelers while performing a data modeling task. Using the notion of cognitive breakdowns, we identify and confirm five types of modeling difficulties relating to different aspects of data modeling by analyzing audiovisual protocols of the modelers' modeling processes, recordings of modelers' interactions with the employed modeling software tool and survey data of modelers about their own perceptions of modeling difficulties they encountered.
  • Konferenzbeitrag
    Pragmatic Reuse for DSML Development
    (Modellierung 2024, 2024) Klikovits, Stefan; Buchs, Didier
    This is a summary of an article (with the same title) originally published in the International Journal on Software and Systems Modeling (Vol. 20, pages 837–866, (2021)) discussing the lessons learned during the development of a DSL for physical resource flows.
  • 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
    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
    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
    Models for Human-Machine Teaming for Shared Decision-Making under Uncertainty
    (Modellierung 2024, 2024) Bencomo, Nelly
    There is growing uncertainty about the runtime environment of software systems. Therefore, how the system should behave under different contexts cannot be fully predicted at design time. It is considerations such as these that have led to the development of self-adaptive systems (SAS), which can dynamically and autonomously reconfigure their behavior to respond to hanging external conditions. The use of Machine Learning (ML) and AI has exacerbated the issues by adding more uncertainty sources. The scope of the talk is in the areas of Model-driven Engineering (MDE), Requirements Engineering (RE), software engineering (SE), and the development of techniques to quantify uncertainty to improve decision-making. The explicit reatment of uncertainty by the running system improves its judgment to make decisions supported by evaluating evidence found during runtime, possibly including the human-in-the-loop. The speaker will discuss how quantification of uncertainty can improve requirement elicitation (using simulations, for example). The talk will also cover different approaches to quantifying uncertainty, models@run.time and their role in Human-Machine Teaming.