Thönssen, Barbara
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Thönssen, Barbara
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- PublikationArchiMEO: 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
- PublikationOntology-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
- PublikationDetermining information relevance based on personalization techniques to meet specific user needs(Springer, 2018) Thönssen, Barbara; Witschel, Hans Friedrich; Rusinov, Oleg; Dornberger, Rolf [in: Business information systems and technology 4.0. New trends in the age of digital change]04A - Beitrag Sammelband
- PublikationA 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
- PublikationBusiness Architecture for Process- Oriented Learning in Public Administration(2015) Silingas, Darius; Thönssen, Barbara; Pierantonio, Alfonso; Efendioglu, Nesat; Woitsch, Robert [in: Business and Dynamic Change: the Arrival of Business Architecture]04 - Beitrag Sammelband oder Konferenzschrift
- PublikationEPOs, an Early Warning System for Post-Merger IS Integration(2015) Unkan, Erol; Thönssen, Barbara [in: 18th International Conference on Business Information Systems, Poznan]04 - Beitrag Sammelband oder Konferenzschrift
- PublikationArchitectures for Business Processess in Organizations(2015) Thönssen, Barbara; Pierantonio, Alfonso; Silingas, Darius; Rosa, Gianni; Woitsch, Robert [in: Procs. Project Showcase (PS’15), Software Technologies: Applications and Foundations (STAF’15)]04B - Beitrag Konferenzschrift
- PublikationDMS - Dokumentenmanagement(BPX, 2014) Hinkelmann, Knut; Thönssen, Barbara02 - Monographie
- PublikationAn approach for coping with risks in contract management(2013) Lutz, Jonas; Thönssen, Barbara; Witschel, Hans Friedrich [in: Knowledge Engineering and Knowledge Management, IC3K 2012]04 - Beitrag Sammelband oder Konferenzschrift
- PublikationTowards 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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