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P348 - Modellierung 2024

https://dl.gi.de/handle/20.500.12116/43610
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Auflistung P348 - Modellierung 2024 nach Erscheinungsdatum

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  • 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
    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
    Model-Driven Engineering for Machine Learning Code Generation using SysML
    (Modellierung 2024, 2024) Rädler, Simon; Rupp, Matthias; Rigger, Eugen; Rinderle-Ma, Stefanie
    The complexity of engineering products increases due to more functions, components, and the number of involved disciplines. In this respect, Data-Driven Engineering (DDE) aims to integrate machine learning to support product development and help manage the increasing complexity of engineered systems. Still, the potential and opportunities of DDE are not entirely reflected in practice, which among others originate from the rarely available machine learning experts on the market and the effort for the implementation in practice. In this respect, this work depicts an approach based on model-driven engineering, allowing to automatically derive executable machine learning code based on machine learning task formalization using the general-purpose modeling language SysML. The main focus of the approach is on the generality of the model transformation using templates so that extensions and changes to the code generation can be integrated without requiring profound modifications to the code generator. The approach is evaluated in a use case in the domain of Cyber-Physical Systems, i.e., weather forecast prediction based on data from a Cyber-Physical weather system. The derived executable code promises to reduce the time for the implementation and supports the standardization of machine learning implementations within a company due to templates.
  • Konferenzbeitrag
    Modeling Classes of Body Sensor Networks
    (Modellierung 2024, 2024) Carwehl, Marc; Reisig, Wolfgang
    Computer-embedded systems frequently manifest in diverse variants, featuring slight differences in interfaces and functionalities, yet fundamentally grounded in a shared functional kernel. To address this variability, we propose to employ a schematic model of the functional kernel, from which concrete system instances are derived. This modeling methodology leverages well-established principles from predicate logic and Petri nets, augmented with the dynamic extensions provided by the \textsc{Heraklit} infrastructure. As a practical case study, we explore the realm of Body Sensor Networks (BSNs), a domain increasingly pivotal in the realm of medical diagnosis. Our work showcases the versatility and adaptability of our modeling framework in the context of BSNs, offering insights into its potential applications in the broader landscape of embedded systems and beyond.
  • Konferenzbeitrag
    Modeling Capabilities of Digital Twin Platforms - Old Wine in New Bottles?
    (Modellierung 2024, 2024) Pfeiffer, Jérôme; Lehner, Daniel; Wortmann, Andreas; Wimmer, Manuel
    This extended abstract summarizes our paper on studying emerging modeling languages provided by digital twin platforms and contrasting them to established object-oriented modeling languages in the field of software engineering. This work has been originally published at the 18th European Conference on Modelling Foundations and Applications (proceedings are published in the Journal of Object Technology (JOT)) in 2022.
  • 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
    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
    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
    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.