Prof. Dr. Andreas Martin
Prof. Dr. Andreas Martin
Tätigkeiten an der FHNW
Andreas Martin ist Professor für Angewandte Künstliche Intelligenz und wissenschaftlicher Mitarbeiter im Studiengang Master of Science in Business Information Systems.
Forschung mit folgenden Themenschwerpunkten:
- Agile Processes
- Knowledge Work
- Semantic Technologies
- Enterprise Architectures and Ontologies
- Enterprise Software Engineering and Architectures
- Information Retrieval and Extraction
- Knowledge Management and Representation
- Natural Language Processing and Computational Linguistics
Andreas Martin ist wissenschaftlicher Mitarbeiter im Studiengang Master of Science in Business Information Systems.
Lehrtätigkeiten von Andreas Martin im Masterstudiengang MSc in Business Information Systems:
- Digitalization of Business Processes
- Pre-Master Information Systems
- Betreuung von Innovation Thinking Project
- Betreuung von Master Thesis Proposal und Master Thesis
Lehrtätigkeiten von Andreas Martin im Bachelorstudiengang BSc in Wirtschaftsinformatik:
- Geschäftsprozessmanagement
- Web Engineering
- Enterprise Software Architectures and Engineering
- Einführung in die Wirtschaftsinformatik
- Betreuung von Studentischen Arbeiten und Bachelor Thesis
Lehrtätigkeiten von Andreas Martin im Bachelorstudiengang BSc in Business Information Technology:
- Web Engineering
- Enterprise Software Architectures and Engineering
- Betreuung von Studentischen Arbeiten und Bachelor Thesis
Lehrtätigkeiten von Andreas Martin im Bachelorstudiengang BSc in Betriebsökonomie:
- Wirtschaftsinformatik II, Theorie, Datenmodellierung und Datenbanken
Themenschwerpunkten
- Agile Processes
- Knowledge Work
- Semantic Technologies
- Enterprise Architectures and Ontologies
- Enterprise Software Engineering and Architectures
- Information Retrieval and Extraction
- Knowledge Management and Representation
- Natural Language Processing and Computational Linguistics
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Keine peer-reviewed Inhalte verfügbar
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Peer-reviewedPande, C., Martin, A., & Pimmer, C. (2023). Towards hybrid dialog management strategies for a health coach chatbot. Proceedings of the AAAI 2023 Spring Symposium on Challenges Requiring the Combination of Machine Learning and Knowledge Engineering (AAAI-MAKE 2023). AAAI 2023 Spring Symposium on Challenges Requiring the Combination of Machine Learning and Knowledge Engineering. https://doi.org/10.26041/fhnw-7453
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Peer-reviewedEichele, S., Hinkelmann, K., & Spahic, M. (2023). Ontology-driven enhancement of process mining with domain knowledge. In A. Martin, H.-G. Fill, A. Gerber, K. Hinkelmann, D. Lenat, R. Stolle, & F. v. Harmelen (Eds.), Proceedings of the AAAI 2023 Spring Symposium on Challenges Requiring the Combination of Machine Learning and Knowledge Engineering (AAAI-MAKE 2023). Sun SITE, Informatik V, RWTH Aachen. https://doi.org/10.26041/fhnw-7371
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Peer-reviewedDuhan, R., Pande, C., & Martin, A. (2023). A flexible, extendable and adaptable model to support AI coaching. In K. Hinkelmann, F. J. López-Pellicer, & A. Polini (Eds.), Perspectives in business informatics research (pp. 172–187). Springer. https://doi.org/10.1007/978-3-031-43126-5_13
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Peer-reviewedSandhu, G., Kilburg, A., Martin, A., Pande, C., Witschel, H. F., Laurenzi, E., & Billing, E. (2022). Practice track: a learning tracker using digital biomarkers for autistic preschoolers. In K. Hinkelmann & A. Gerber (Eds.), Proceedings of the Society 5.0 Conference 2022 - Integrating digital world and real world to resolve challenges in business and society (pp. 219–230). https://doi.org/10.29007/m2jx
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Peer-reviewedPrater, R., & Laurenzi, E. (2022). A hybrid intelligent approach for the support of higher education students in literature discovery. In A. Martin, K. Hinkelmann, H.-G. Fill, A. Gerber, D. Lenat, R. Stolle, & F. van Harmelen (Eds.), Proceedings of the AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022). https://doi.org/10.26041/fhnw-7307
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Peer-reviewedPande, C., Witschel, H. F., & Martin, A. (2022). New hybrid techniques for business recommender systems. Applied Sciences, 12(10). https://doi.org/10.3390/app12104804
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Peer-reviewedPande, C., Witschel, H. F., Martin, A., & Montecchiari, D. (2021). Hybrid conversational AI for intelligent tutoring systems. In A. Martin, K. Hinkelmann, H.-G. Fill, A. Gerber, D. Lenat, R. Stolle, & F. v. Harmelen (Eds.), Proceedings of the AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021). Sun SITE, Informatik V, RWTH Aachen. https://doi.org/10.26041/fhnw-6988
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Peer-reviewedHinkelmann, K., Laurenzi, E., Martin, A., Montecchiari, D., Spahic, M., & Thönssen, B. (2020). ArchiMEO: A standardized enterprise ontology based on the ArchiMate conceptual model. In S. Hammoudi, L. Ferreira Pires, & B. Selić (Eds.), Proceedings of the 8th International Conference on Model-Driven Engineering and Software Development (Vol. 1, pp. 417–424). https://doi.org/10.5220/0009000204170424
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Peer-reviewedTelesko, R., Jüngling, S., & Gachnang, P. (2020). Combining symbolic and sub-symbolic AI in the context of education and learning. In A. Martin, K. Hinkelmann, H.-G. Fill, A. Gerber, D. Lenat, R. Stolle, & F. van Harmelen (Eds.), Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020) (Vol. 1). https://doi.org/10.26041/fhnw-6675
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Witschel, H. F., Pande, C., Martin, A., Laurenzi, E., & Hinkelmann, K. (2020). Visualization of patterns for hybrid learning and reasoning with human involvement. In R. Dornberger (Ed.), New trends in business information systems and technology (pp. 193–204). Springer. https://doi.org/10.1007/978-3-030-48332-6_13
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Peer-reviewedJüngling, S., Peraic, M., & Martin, A. (2020). Towards AI-based solutions in the system development lifecycle. In A. Martin, K. Hinkelmann, H.-G. Fill, A. Gerber, D. Lenat, R. Stolle, & F. van Harmelen (Eds.), Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020) (Vol. 1). https://doi.org/10.26041/fhnw-6680
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Peer-reviewedWitschel, H. F., & Martin, A. (2019). Learning and engineering similarity functions for business recommenders. In A. Martin, K. Hinkelmann, A. Gerber, D. Lenat, F. v. Harmelen, & P. Clark (Eds.), Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019). https://doi.org/10.26041/fhnw-6789
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Peer-reviewedLaurenzi, E., Hinkelmann, K., Jüngling, S., Montecchiari, D., Pande, C., & Martin, A. (2019). Towards an assistive and pattern learning-driven process modeling approach. In A. Martin, K. Hinkelmann, A. Gerber, D. Lenat, F. van Harmelen, & P. Clark (Eds.), Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019). https://doi.org/10.26041/fhnw-6431
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Peer-reviewedMartin, A., Hinkelmann, K., Gerber, A., Lenat, D., Harmelen, F. v., & Clark, P. (2019). Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019). CEUR Workshop Proceedings. https://irf.fhnw.ch/handle/11654/42497
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Peer-reviewedJüngling, S., & Hofer, A. (2019). Leverage white-collar workers with AI. In A. Martin, K. Hinkelmann, A. Gerber, D. Lenat, & P. Clark (Eds.), Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019). https://doi.org/10.26041/fhnw-6456
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Peer-reviewedHinkelmann, K., Blaser, M., Faust, O., Horst, A., & Mehli, C. (2019). Virtual bartender: a dialog system combining data-driven and knowledge-based recommendation. In A. Martin, K. Hinkelmann, A. Gerber, D. Lenat, F. van Harmelen, & P. Clark (Eds.), Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019). https://doi.org/10.26041/fhnw-6603
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Peer-reviewedBaldini, I., Barrett, C., Chella, A., Cinelli, C., Gamez, D., Gilpin, L. H., Hinkelmann, K., Holmes, D., Kido, T., Kocaoglu, M., Lawless, W. F., Lomuscio, A., Macbeth, J. C., Martin, A., Mittu, R., Patterson, E., Sofge, D., Tadepalli, P., Takadama, K., & Wilson, S. (2019). Reports of the AAAI 2019 Spring Symposium Series. AI Magazine, 40(3), 59–66. https://doi.org/10.1609/aimag.v40i3.5181
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Peer-reviewedAhmed, S., Hinkelmann, K., & Corradini, F. (2019). Combining machine learning with knowledge engineering to detect fake news in social networks - A survey. In A. Martin, A. Martin, K. Hinkelmann, A. Gerber, D. Lenat, & F. van Harmelen (Eds.), Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019). https://doi.org/10.26041/fhnw-6765
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Peer-reviewedRudolf von Rohr, C., Witschel, H. F., & Martin, A. (2018). Training and re-using human experience: a recommender for more accurate cost estimates in project planning. IC3K 2018 - Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 52–62. https://doi.org/10.5220/0006893200520062
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Hinkelmann, K., Laurenzi, E., Martin, A., & Thönssen, B. (2018). Ontology-based metamodeling. In R. Dornberger (Ed.), Business information systems and technology 4.0. New trends in the age of digital change (pp. 177–194). Springer. https://doi.org/10.1007/978-3-319-74322-6_12
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Martin, A., & Hinkelmann, K. (2018). Case-based reasoning for process experience. In R. Dornberger (Ed.), Business information systems and technology 4.0. New trends in the age of digital change (pp. 47–63). Springer. https://doi.org/10.1007/978-3-319-74322-6_4
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Peer-reviewedWitschel, H. F., & Martin, A. (2018). Random walks on human knowledge: incorporating human knowledge into data-driven recommenders. In J. Bernardino, A. Salgado, & J. Filipe (Eds.), IC3K 2018. 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. Proceedings (Vol. 3, pp. 61–70). https://doi.org/10.5220/0006893900630072
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Peer-reviewedMartin, A., Emmenegger, S., Hinkelmann, K., & Thönssen, B. (2016). A viewpoint-based case-based reasoning approach utilising an enterprise architecture ontology for experience management. Enterprise Information Systems, 11(4), 551–575. https://doi.org/10.1080/17517575.2016.1161239
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Peer-reviewedEmmenegger, S., Thönssen, B., Laurenzi, E., Martin, A., Zhang Sprenger, C., Hinkelmann, K., & Witschel, H. F. (2016). An Ontology-based and Case-based Reasoning supported Workplace Learning Approach. Communications in Computer and Information Science. http://hdl.handle.net/11654/24551
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Peer-reviewedEmmenegger, S., Lutz, J., Witschel, H. F., & Martin, A. (2015). A new Retrieval Function for Ontology-Based Complex Case Descriptions. Proceedings of CBR-MD′15, 2015. https://doi.org/10.26041/fhnw-57
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Peer-reviewedCognini, R., Hinkelmann, K., & Martin, A. (2015). A Case Modelling Language for Process Variant Management in Case-based Reasoning. AdaptiveCM 2015 – 4th International Workshop on Adaptive Case Management and Other Non-Workflow Approaches to BPM. http://hdl.handle.net/11654/10753
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Peer-reviewedMartin, A., Emmenegger, S., & Wilke, G. (2013, November 8). Integrating an Enterprise Architecture Ontology in a Case-Based Reasoning Approach for Project Knowledge. ES 2013 : The First Enterprise Systems Conference. http://hdl.handle.net/11654/8993
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Martin, A. (2013). Wissensarbeit ist nicht Routinearbeit: Prozesse - Zwängerei für Wissensarbeitende. Unternehmer Zeitung, 2013(1/2), 32–33. https://doi.org/10.26041/fhnw-3254
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Peer-reviewedBrander, S., Hinkelmann, K., Martin, A., & Thönssen, B. (2011, March 21). Mining of Agile Business Processes. Proceedings of the AAAI 2011 Spring Symposium. AAAI 2011 Spring Symposium. http://hdl.handle.net/11654/9617
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Peer-reviewedBrander, S., Hinkelmann, K., Hu, B., Martin, A., Riss, U., Thönssen, B., & Witschel, H. F. (2011). Refining process models through the analysis of informal work practice. 9th International Conference on Business Process Management (BPM 2011). http://hdl.handle.net/11654/9162
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Brun, R., & Martin, A. (2010, June 3). Agile Process Execution with KISSmir. SBPM 2010 5th International Workshop on Semantic Business Process Management collocated with ESWC 2010. http://hdl.handle.net/11654/9609
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Martin, A., & Brun, R. (2009). Applying Organizational Learning to Enterprise Knowledge Maturing. 39–45. http://hdl.handle.net/11654/9552
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Brun, R., & Martin, A. (2009). Support Knowledge Intensive Work with Semantic Technologies. IBIC′09 International Business Informatics Challenge and Conference 2009. IBIC′09 International Business Informatics Challenge and Conference 2009. https://doi.org/10.26041/fhnw-3187
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Keine peer-reviewed Inhalte verfügbar
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sic!
1.8.2012–31.12.2014, Martin. Andreas
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Keine peer-reviewed Inhalte verfügbar
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Peer-reviewedPande, C., Witschel, H. F., & Martin, A. (2021). ChEdventure - A chatbot-based educational adventure game for modeling tasks in information systems. 5th International Workshop on Chatbot Research (CONVERSATIONS 2021). https://irf.fhnw.ch/handle/11654/43134
Kontakt
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Prof. Dr. Andreas Martin
- Professor für Angewandte Künstliche Intelligenz, Studiengang MSc Business Information Systems
- Telefonnummer
- +41 62 957 23 47 (Direkt)
- YW5kcmVhcy5tYXJ0aW5AZmhudy5jaA==
- Fachhochschule Nordwestschweiz FHNW
Hochschule für Wirtschaft
Riggenbachstrasse 16
4600 Olten