Martin, Andreas

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Martin
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Andreas
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Martin, Andreas

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2016 - 20204
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Autor:in: Laurenzi, Emanuele × Dateien dazu vorhanden?: Nein ×

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  • Vorschaubild nicht verfügbar
    Publikation
    Visualization of patterns for hybrid learning and reasoning with human involvement
    (Springer, 2020) Witschel, Hans Friedrich; Pande, Charuta; Martin, Andreas; Laurenzi, Emanuele; Hinkelmann, Knut; Dornberger, Rolf [in: New trends in business information systems and technology]
    04A - Beitrag Sammelband
  • Vorschaubild nicht verfügbar
    Publikation
    ArchiMEO: A standardized enterprise ontology based on the ArchiMate conceptual model
    (2020) Hinkelmann, Knut; Laurenzi, Emanuele; Martin, Andreas; Montecchiari, Devid; Spahic, Maja; Thönssen, Barbara; Hammoudi, Slimane; Ferreira Pires, Luis; Selić, Bran [in: Proceedings of the 8th International Conference on Model-Driven Engineering and Software Development]
    Many enterprises face the increasing challenge of sharing and exchanging data from multiple heterogeneous sources. Enterprise Ontologies can be used to effectively address such challenge. In this paper, we present an Enterprise Ontology called ArchiMEO, which is based on an ontological representation of the ArchiMate standard for modeling Enterprise Architectures. ArchiMEO has been extended to cover various application domains such as supply risk management, experience management, workplace learning and business process as a service. Such extensions have successfully proven that our Enterprise Ontology is beneficial for enterprise applications integration purposes.
    04B - Beitrag Konferenzschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Ontology-based metamodeling
    (Springer, 2018) Hinkelmann, Knut; Laurenzi, Emanuele; Martin, Andreas; Thönssen, Barbara; Dornberger, Rolf [in: Business information systems and technology 4.0. New trends in the age of digital change]
    Decision makers use models to understand and analyze a situation, to compare alternatives and to find solutions. Additionally, there are systems that support decision makers through data analysis, calculation or simulation. Typically, modeling languages for humans and machine are different from each other. While humans prefer graphical or textual models, machine-interpretable models have to be represented in a formal language. This chapter describes an approach to modeling that is both cognitively adequate for humans and processable by machines. In addition, the approach supports the creation and adaptation of domain-specific modeling languages. A metamodel which is represented as a formal ontology determines the semantics of the modeling language. To create a graphical modeling language, a graphical notation can be added for each class of the ontology. Every time a new modeling element is created during modeling, an instance for the corresponding class is created in the ontology. Thus, models for humans and machines are based on the same internal representation.
    04A - Beitrag Sammelband
  • Vorschaubild nicht verfügbar
    Publikation
    An Ontology-based and Case-based Reasoning supported Workplace Learning Approach
    (Springer, 2016) Emmenegger, Sandro; Thönssen, Barbara; Laurenzi, Emanuele; Martin, Andreas; Zhang Sprenger, Congyu; Hinkelmann, Knut; Witschel, Hans Friedrich [in: Communications in Computer and Information Science]
    01A - Beitrag in wissenschaftlicher Zeitschrift