Laurenzi, Emanuele

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Laurenzi
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Emanuele
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Laurenzi, Emanuele

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2016 - 20183
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Autor:in: Thönssen, Barbara × Enddatum: 2019 ×

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  • 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
  • Vorschaubild
    Publikation
    Workplace Learning - Providing Recommendations of Experts and Learning Resources in a Context-sensitive and Personalized Manner
    (2016) Emmenegger, Sandro; Laurenzi, Emanuele; Thönssen, Barbara; Zhang Sprenger, Congyu; Hinkelmann, Knut; Witschel, Hans Friedrich [in: Proceedings of Special Session on Learning Modeling in Complex Organizations (LCMO) at MODELSWARD'16]
    Support of workplace learning is increasingly important as change in every form determines today's working world in industry and public administrations alike. Adapt quickly to a new job, a new task or a new team is a major challenge that must be dealt with ever faster. Workplace learning differs significantly from school learning as it should be strictly aligned to business goals. In our approach we support workplace learning by providing recommendations of experts and learning resources in a context-sensitive and personalized manner. We utilize user s' workplace environment, we consider their learning preferences and zone of proximal development, and compare required and acquired competencies in order to issue the best suited recommendations. Our approach is part of the European funded project Learn PAd. Applied research method is Design Science Research. Evaluation is done in an iterative process. The recommender system introduced here is evaluated theoretically based on user requirements and practically in an early evaluation process conducted by the Learn PAd application partner.
    04B - Beitrag Konferenzschrift