Guldner, A.; Kreten, S.; Naumann, S: Exploration and systematic assessment of the resource efficiency of Machine Learning. Gesellschaft für Informatik e.V. (GI) (Hrsg.) INFORMATIK 2021 - Computer Science & Sustainability, pp. 287–299, Lecture Notes in Informatics (LNI), Bonn, 2021. dx.doi.org/10.18420/informatik2021-023 

Hoffmann, M.; Malburg, L.; and Bergmann, R. In Marrella, A.; and Weber, B.: ProGAN: Toward a Framework for Process Monitoring and Flexibility by Change via Generative Adversarial Networks, Business Process Management Workshops - BPM 2021 International Workshops, Rome, Italy, September 6 - 10, 2021, of Lecture Notes in Business Information Processing, 2021. Springer. 

Malburg, L.; Hoffmann, M.; Trumm, S.; and Bergmann, R.: Improving Similarity-Based Retrieval Efficiency by Using Graphic Processing Units in Case-Based Reasoning. In: Proceedings of the 34th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2021, North Miami Beach, Florida, USA, 2021 

Malburg, L.; Rieder, M.; Seiger, R.; Klein, P.; and Bergmann, R.: Object Detection for Smart Factory Processes by Machine Learning. Procedia Computer Science, 184: 581–588. 2021. 

Rietzke, E.; Maletzki, C.; Bergmann, R.; Kuhn, N.: Execution of Knowledge-Intensive Processes by Utilizing Ontology-Based Reasoning. J Data Semantics 10, 3–18 (2021). doi.org/10.1007/s13740-021-00127-w 

Theusch, F.; Klein, P.; Bergmann, R.; Wilke, W.; Bock, W.; Weber, A.: Fault Detection and Condition Monitoring in District Heating Using Smart Meter Data. In: Proceedings of the European Conference of the PHM Society 2021, pp. 407 – 417