Foto: Anna Logue
Dr. Christian Bartelt
Foto: Karin Glückler
Geschäftsführer des Institut für Enterprise Systems
Forschungsinteressen
- Software technology: Architectures and technologies to ensuring dependability of smart and dynamic software systems
- Process management: Model-based engineering of smart processes in business, production and development of enterprises
- Software systems engineering: Artificial intelligence applications in software and systems engineering
Lebenslauf
| Seit 2015 | Leiter des Forschungsclusters „Intelligent Processes“ am InES (Universität Mannheim) |
|---|---|
| Seit 2012 | Dozent an der Technischen Universität Clausthal |
| Seit 2011 | Dozent an der Universität Hildesheim |
| 2013–2014 | Koordinator of the Cooperative Doctorial Program E-Mobility (University of Braunschweig, Clausthal, Hannover, Ostfalia) |
| 2011–2015 | Postdoc am Institute for Software and Systems Engineering (Technische Universität Clausthal ) |
| 2007–2011 | Wissenschaftlicher Mitarbeiter am Institute for Software and Systems Engineering (Technische Universität Clausthal) |
| 2004–2007 | Wissenschaftlicher Mitarbeiter am Lehrstuhl Software Architecture (Technische Universität Kaiserslautern) |
| 1997–2004 | Studium Computer Science an der Technischen Universität Kaiserslautern |
Publikationen
- Marton, S., Lüdtke, S. und Bartelt, C. (2022). Explanations for neural networks by neural networks. Applied Sciences, 12, 1–14.
- Niemann, F., Lüdtke, S., Bartelt, C. und Ten Hompel, M. (2022). Context-aware human activity recognition in industrial processes. Sensors, 22, 1–14.
- Burzlaff, F., Wilken, N., Bartelt, C. und Stuckenschmidt, H. (2019). Semantic interoperability methods for smart service systems: A survey. IEEE Transactions on Engineering Management : EM, 1–15.
- Schreckenberger, C., He, Y., Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2023). Online random eature forests for learning in varying feature spaces. In , Proceedings of the 37th AAAI Conference on Artificial Intelligence (S. tba-tba). , AAAI Press: Washington, DC.
- Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2022). Exchangeability-aware sum-product networks. In , Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, Vienna, 23–29 July 2022 (S. 4864–4870). , International Joint Conferences on Artificial Intelligence Organization: Wien.
- Schreckenberger, C., Bartelt, C. und Stuckenschmidt, H. (2022). Dynamic forest for learning from data streams with varying feature spaces. In , Cooperative information systems : 28th International Conference, CoopIS 2022, Bozen-Bolzano, Italy, October 4–7, 2022, Proceedings (S. 95–111). Lecture Notes in Computer Science, Springer: Berlin [u.a.].
- Wilken, N., Cohausz, L., Schaum, J., Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2022). Leveraging planning landmarks for hybrid online goal recognition. In , International Conference on Automated Planning and Scheduling ICAPS (2022) : June 13–17, 2022, virtual (S. ). , CEUR Workshop Proceedings: Aachen.
- Burzlaff, F. und Bartelt, C. (2021). Knowledge-driven architecture composition: Assisting the system integrator to reuse integration knowledge. In , ICWE 2021 : 21st International Conference on Web Engineering, Biarritz, France, May 18–21, 2021 ; proceedings (S. ). Lecture Notes in Computer Science, Springer: Berlin [u.a.].
- Hoffmann, L., Bartelt, C. und Stuckenschmidt, H. (2021). Knowledge injection via ML-based initialization of neural networks. In , Proceedings of the CIKM 2021 Workshops (CIKMW 2021) co-located with 30th ACM International Conference on Information and Knowledge Management (CIKM 2021) : Gold Coast, Queensland, Australia, November 1–5,2021 (S. 1–6). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
- Kolthoff, K., Bartelt, C. und Ponzetto, S. P. (2021). Automated Retrieval of Graphical User Interface Prototypes from Natural Language Requirements. In , NLDB 2021 : 26th International Conference on Natural Language & Information Systems, Saarbrücken, Germany, June 23–25, 2021 ; proceedings (S. 376–384). Lecture Notes in Computer Science, Springer: Berlin [u.a.].
- Nolte, F., Wilken, N. und Bartelt, C. (2021). Rendezvous delivery: Utilizing autonomous electric vehicles to improve the efficiency of last mile parcel delivery in urban areas. In , 2021 IEEE PerCom Workshops : PerAwareCity 2021, 6th IEEE Workshop on Pervasive Context-Aware Smart Cities and Intelligent Transportation Systems, March 22–26, 2021 in Kassel, Germany (S. 148–153). , IEEE Computer Society: Piscataway, NJ.
- Pernpeintner, M., Bartelt, C. und Stuckenschmidt, H. (2021). Governing black-box agents in competitive multi-agent systems. In , Multi-Agent Systems : 18th European Conference, EUMAS 2021, virtual event, June 28–29, 2021, revised selected papers (S. 19–36). Lecture Notes in Computer Science, Springer: Berlin [u.a.].
- Wilken, N., Stuckenschmidt, H. und Bartelt, C. (2021). Combining symbolic and data-driven methods for goal recognition. In , 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) (S. 428–429). , IEEE Computer Society: Piscataway, NJ.
- Burzlaff, F., Bongarth, B., Grottker, S., Hammen, J. und Bartelt, C. (2020). MergePoint: A graphical web-app for merging HTTP-endpoints and IoT-platform models. In , 53rd Hawaii International Conference on System Sciences, HICSS 2020 : Maui, Hawaii, USA, January 7–10, 2020 (S. 1–10). Proceedings of the 53rd Hawaii International Conference on System Sciences, ScolarSpace: Honolulu, HI.
- Kolthoff, K., Bartelt, C. und Ponzetto, S. P. (2020). GUI2WiRe: Rapid wireframing with a mined and large-scale GUI repository using natural language requirements.
In , ASE 2020 : 35th IEEE/
ACM International Conference on Automated Software Engineering : Mon 21 – Fri 25 September 2020, Melbourne, Australia : Tool Demonstrations (S. 1297–1301). , ACM: New York, NY. - Schreckenberger, C., Bartelt, C. und Stuckenschmidt, H. (2020). Robust decision tree induction from unreliable data sources. In , STAIRS 2020 : Proceedings of the 9th European Starting AI Researchers' Symposium 2020 co-located with 24th European Conference on Artificial Intelligence (ECAI 2020) Santiago Compostela, Spain, August, 2020 (S. Paper 6, 1–8). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
- Schreckenberger, C., Glockner, T., Stuckenschmidt, H. und Bartelt, C. (2020). Restructuring of Hoeffding trees for Trapezoidal Data Streams. In , 20th IEEE International Conference on Data Mining Workshops : 17–20 November 2020, Virtual Conference : Proceedings (S. 416–423). , IEEE: Los Alamitos, CA [u.a.].
- Burzlaff, F., Ackel, M. und Bartelt, C. (2019). A mapping language for IoT device descriptions. In , 2019 IEEE 43rd Annual Computer Software and Applications Conference : 15–19 July 2019, Milwaukee, Wisconsin : proceedings (S. 115–120). , IEEE Computer Society: Piscataway, NJ.
- Burzlaff, F. und Bartelt, C. (2019). A conceptual architecture for enabling future self-adaptive service systems. In , 52nd Hawaii International Conference on System Sciences, HICSS 2019, Grand Wailea, Maui, Hawaii, USA, January 8–11, 2019 (S. 1–10). , AISeL: Atlanta, GA.
- Schreckenberger, C., Bartelt, C. und Stuckenschmidt, H. (2019). Enhancing a crowd-based delivery network with mobility predictions. In , PredictGIS'19 : Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility : Chicago, IL, USA, November 05, 2019 (S. 66–75). Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility, ACM: New York, NY.
- Schreckenberger, C., Bartelt, C. und Stuckenschmidt, H. (2019). iDropout: Leveraging deep taylor decomposition for the robustness of deep neural networks. In , On the Move to Meaningful Internet Systems: OTM 2019 Conferences : Confederated International Conferences: CoopIS, ODBASE, C&TC 2019, Rhodes, Greece, October 21–25, 2019, Proceedings (S. 113–126). Lecture Notes in Computer Science, Springer: Berlin [u.a.].
- Schreckenberger, C., Beckmann, S. und Bartelt, C. (2019). Next place prediction: A systematic literature review. In , PredictGIS 2018 : Proceedings of the 2nd ACM SIGSPATIAL Workshop on Prediction of Human Mobility : ACM GIS 2018 Conference: November 6 – November 9, 2018, Seattle, Washington (S. 37–45). Proceedings of the 2Nd ACM SIGSPATIAL Workshop on Prediction of Human Mobility, ACM: New York, NY.
- Burzlaff, F. und Bartelt, C. (2018). I4.0-device integration: A qualitative analysis of methods and technologies utilized by system integrators: Implications for enginering future industrial internet of things system. In , 2018 IEEE 15th International Conference on Software Architecture companion : ICSA-C 2018 : proceedings : 30 April-4 May 2018, Seattle, Washington (S. 27–34). , IEEE: Piscataway, NJ.
- Burzlaff, F., Bartelt, C. und Adler, L. (2018). Towards automating service matching for manufacturing systems: Exemplifying knowledge-driven architecture composition. In , 51st CIRP Conference on Manufacturing Systems (CIRP CMS 2018), Stockholm, Sweden, 16–18 May 2018 (S. 707–713). Procedia CIRP, Elsevier ; Curran: Amsterdam ; Red Hook, NY.
- Burzlaff, F., Bartelt, C. und Jacobs, S. (2018). Executing model-based software development for embedded I4.0 devices properly. In , MOD-WS 2018 : Joint Proceedings of the Workshops at Modellierung 2018 co-located with Modellierung 2018, Braunschweig, Germany, February 21, 2018 (S. 35–46). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
- Burzlaff, F., Bartelt, C. und Stuckenschmidt, H. (2018). Next steps in knowledge-driven architecture composition. In , LWDA 2018 : Proceedings of the Conference „Lernen, Wissen, Daten, Analysen“ Mannheim, Germany, August 22–24, 2018 (S. 78–83). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
- Burzlaff, F. und Bartelt, C. (2017). Knowledge-driven architecture composition: Case-based formalization of integration knowledge to enable automated component coupling. In , ICSA 2017 : 2017 IEEE International Conference on Software Architecture : side track proceedings : proceedings : 3–7 April 2017, Gothenburg, Sweden (S. 108–111). , IEEE: Piscataway, NJ.
- Marton, S., Bartelt, C. und Stuckenschmidt, H. (2020). Machine learning for converting Black-Box models to interpretable functions. PhD Forum, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2020, Online.
- Schreckenberger, C., Bartelt, C. und Stuckenschmidt, H. (2020). Tree-based learning for dynamic data streams. PhD Forum, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2020, Online.
- Oesterle, M., Bartelt, C., Lüdtke, S. und Stuckenschmidt, H. (2022). Self-learning governance of black-box multi-agent systems. In Coordination, Organizations, Institutions, Norms, and Ethics for Governance of Multi-Agent Systems XV : International Workshop, COINE 2022, virtual event, May 9, 2022, revised selected papers (S. 73–91). Berlin [u.a.]: Springer.
Andere Publikationen
- [Standard] V-Modell®XT – Vorgehensmodell für die Durchführung von IT-Projekten, insb. zur Entwicklung von Softwaresystemen