Laurenzi, Emanuele

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Laurenzi
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Emanuele
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Laurenzi, Emanuele

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2016 - 20193
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Autor:in: Martin, Andreas × Enddatum: 2019 × Anfangsdatum: 2016 × Hochschule: Hochschule für Wirtschaft ×

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  • Vorschaubild
    Publikation
    Towards an assistive and pattern learning-driven process modeling approach
    (2019) Laurenzi, Emanuele; Hinkelmann, Knut; Jüngling, Stephan; Montecchiari, Devid; Pande, Charuta; Martin, Andreas; Martin, Andreas; Hinkelmann, Knut; Gerber, Aurona; Lenat, Doug; van Harmelen, Frank; Clark, Peter [in: Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019)]
    The practice of business process modeling not only requires modeling expertise but also significant domain expertise. Bringing the latter into an early stage of modeling contributes to design models that appropriately capture an underlying reality. For this, modeling experts and domain experts need to intensively cooperate, especially when the former are not experienced within the domain they are modeling. This results in a time-consuming and demanding engineering effort. To address this challenge, we propose a process modeling approach that assists domain experts in the creation and adaptation of process models. To get an appropriate assistance, the approach is driven by semantic patterns and learning. Semantic patterns are domain-specific and consist of process model fragments (or end-to-end process models), which are continuously learned from feedback from domain as well as process modeling experts. This enables to incorporate good practices of process modeling into the semantic patterns. To this end, both machine-learning and knowledge engineering techniques are employed, which allow the semantic patterns to adapt over time and thus to keep up with the evolution of process modeling in the different business domains.
    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