Martin, Andreas
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- PublikationVisualization 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
- 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
- PublikationReports of the AAAI 2019 Spring Symposium Series(American Association for Artificial Intelligence, 2019) Baldini, Ioana; Barrett, Clark; Chella, Antonio; Cinelli, Carlos; Gamez, David; Gilpin, Leilani H.; Hinkelmann, Knut; Holmes, Dylan; Kido, Takashi; Kocaoglu, Murat; Lawless, William F.; Lomuscio, Alessio; Macbeth, Jamie C.; Martin, Andreas; Mittu, Ranjeev; Patterson, Evan; Sofge, Donald; Tadepalli, Prasad; Takadama, Keiki; Wilson, Shomir [in: AI Magazine]The AAAI 2019 Spring Series was held Monday through Wednesday, March 25–27, 2019 on the campus of Stanford University, adjacent to Palo Alto, California. The titles of the nine symposia were Artificial Intelligence, Autonomous Machines, and Human Awareness: User Interventions, Intuition and Mutually Constructed Context; Beyond Curve Fitting — Causation, Counterfactuals and Imagination-Based AI; Combining Machine Learning with Knowledge Engineering; Interpretable AI for Well-Being: Understanding Cognitive Bias and Social Embeddedness; Privacy- Enhancing Artificial Intelligence and Language Technologies; Story-Enabled Intelligence; Towards Artificial Intelligence for Collaborative Open Science; Towards Conscious AI Systems; and Verification of Neural Networks.01A - Beitrag in wissenschaftlicher Zeitschrift
- PublikationProceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019)(CEUR Workshop Proceedings, 2019) Martin, Andreas; Hinkelmann, Knut; Gerber, Aurona; Lenat, Doug; Harmelen, Frank van; Clark, Peter03 - Sammelband
- PublikationCase-based reasoning for process experience(Springer, 2018) Martin, Andreas; Hinkelmann, Knut; Dornberger, Rolf [in: Business information systems and technology 4.0. New trends in the age of digital change]The following chapter describes an integrated case-based reasoning (CBR) approach to process learning and experience management. This integrated CBR approach reflects domain knowledge and contextual information based on an enterprise ontology. The approach consists of a case repository, which contains experience items described using a specific case model. The case model reflects, on the one hand, the process logic, i.e. the flow of work, and on the other the business logic, which is the knowledge that can be used to achieve a result.04A - Beitrag Sammelband
- 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
- 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
- PublikationAn 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
- PublikationA Case Modelling Language for Process Variant Management in Case-based Reasoning(2015) Cognini, Riccardo; Hinkelmann, Knut; Martin, Andreas [in: AdaptiveCM 2015 – 4th International Workshop on Adaptive Case Management and other non-workflow approaches to BPM]04B - Beitrag Konferenzschrift
- PublikationMining of Agile Business Processes(21.03.2011) Brander, Simon; Hinkelmann, Knut; Martin, Andreas; Thönssen, Barbara [in: Proceedings of the AAAI 2011 Spring Symposium]Organizational agility is a key challenge in today's business world. The Knowledge-Intensive Service Support approach tackles agility by combining process modeling and business rules. In the paper at hand, we present five approaches of process mining that could further increase the agility of processes by improving an existing process model.04B - Beitrag Konferenzschrift