Thönssen, Barbara

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Thönssen
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Barbara
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Thönssen, Barbara

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2012 - 20164
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Autor:in: Emmenegger, Sandro × Thema: 330 - Wirtschaft ×

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  • Vorschaubild nicht verfügbar
    Publikation
    A viewpoint-based case-based reasoning approach utilising an enterprise architecture ontology for experience management
    (Taylor & Francis, 28.03.2016) Martin, Andreas; Emmenegger, Sandro; Hinkelmann, Knut; Thönssen, Barbara [in: Enterprise Information Systems]
    The accessibility of project knowledge obtained from experiences is an important and crucial issue in enterprises. This information need about project knowledge can be different from one person to another depending on the different roles he or she has. Therefore, a new ontology-based case-based reasoning (OBCBR) approach that utilises an enterprise ontology is introduced in this article to improve the accessibility of this project knowledge. Utilising an enterprise ontology improves the case-based reasoning (CBR) system through the systematic inclusion of enterprise-specific knowledge. This enterprise-specific knowledge is captured using the overall structure given by the enterprise ontology named ArchiMEO, which is a partial ontological realisation of the enterprise architecture framework (EAF) ArchiMate. This ontological representation, containing historical cases and specific enterprise domain knowledge, is applied in a new OBCBR approach. To support the different information needs of different stakeholders, this OBCBR approach has been built in such a way that different views, viewpoints, concerns and stakeholders can be considered. This is realised using a case viewpoint model derived from the ISO/IEC/IEEE 42010 standard. The introduced approach was implemented as a demonstrator and evaluated using an application case that has been elicited from a business partner in the Swiss research project.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Towards a Procedure for Assessing Supply Chain Risks Using Semantic Technology
    (Springer, 2013) Emmenegger, Sandro; Hinkelmann, Knut; Laurenzini, Emanuele; Thönssen, Barbara; Fred, Ana; Dietz, Jan L. G; Liu, Kecheng; Felipe, Joaquim [in: Knowledge Discovery, Knowledge Engineering and Knowledge Management]
    In the APPRIS project an Early-Warning-System (EWS) is developed applying semantic technologies, namely an enterprise ontology and an inference engine, for the assessment of procurement risks. Our approach allows for analyzing internal resources (e.g. ERP and CRM data) and external sources (e.g. entries in the Commercial Register and newspaper reports) to assess known risks, but also for identifying black swans, which hit enterprises with no warning but potentially large impact. For proof of concept we developed a prototype that allows for integrating data from various information sources, of various information types (structured and unstructured), and information quality (assured facts, news); automatic identification, validation and quantification of risks and aggregation of assessment results on several granularity levels. The motivating scenario is derived from three business project partners real requirements for an EWS.
    04B - Beitrag Konferenzschrift
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    Publikation
    Improving supply-chain-management based on semantically enriched risk descriptions
    (04.10.2012) Emmenegger, Sandro; Laurenzini, Emanuele; Thönssen, Barbara [in: Proceedings of 4th Conference on Knowledge Management and Information Sharing (KMIS2012)]
    04B - Beitrag Konferenzschrift
  • Vorschaubild
    Publikation
    Die Risiken entdecken, bevor sie entstehen.
    (Swiss Professional Media, 04.04.2012) Thönssen, Barbara; Emmenegger, Sandro [in: Unternehmer Zeitung]
    Um Risiken in der Supply Chain früh zu entdecken, genügt es nicht, eigene ERP-Daten für Lieferanten auszuwerten. Vielmehr muss das gesamte Netzwerk betrachtet und Daten aus externen Quellen mit internen Daten verknüpft werden. Mit Hilfe semantischer Technologien können Daten integriert, automatisch analysiert und so Risiken früher identifiziert werden.
    01B - Beitrag in Magazin oder Zeitung