Dr. David Broneske did his Bachelor and Master in Computer Science at the Otto-von-Guericke University Magdeburg, where he received his Ph.D. in 2019 and afterward started his Habilitation. From 2019 to 2020, he was the substitutional head of the chair of Database and Informationssystems at Hochschule Anhalt in Köthen. Since March 2021, he is the acting head of Department 4 "Infrastructures and Methods" of the German Centre for Higher Education Research and Science Studies (DZHW).
Dr. David Broneske
Research Area Research Infrastructure and Methods
Acting Head of Department
- +49 511 450670-454
- Google Scholar
- Orcid
Academic research fields
Research Data Management for Learning Analytics Data, Main-Memory Database Systems on Modern Hardware, Interactive Data Exploration and Visualization, Artificial Intelligence for Data Cleaning and Analysis
Projects
List of projects
Publications
List of publications
Embracing NVM: Optimizing $B^𝜖$-tree structures and data compression in storage engines.Karim, S., Wünsche, F., Broneske, D., Kuhn, M., & Saake, G. (2025).Embracing NVM: Optimizing $B^𝜖$-tree structures and data compression in storage engines. In Binnig, C. et al. (Hrsg.), Datenbanksysteme für Business, Technologie und Web - Workshopband (BTW 2025) (S. 329-333). Bonn: Gesellschaft für Informatik. https://doi.org/10.18420/BTW2025-137 |
A multi-objective evolutionary algorithm for detecting protein complexes in PPI networks using gene ontology.Abbas, M. N., Broneske, D., & Saake, G. (2025).A multi-objective evolutionary algorithm for detecting protein complexes in PPI networks using gene ontology. Scientific Reports, 15. https://doi.org/10.1038/s41598-025-01667-y |
AutoML meets hugging face: Domain-aware pretrained model selection for text classification.Safikhani, P., & Broneske, D. (2025).AutoML meets hugging face: Domain-aware pretrained model selection for text classification. In A. Ebrahimi, S. Haider, E. Liu, M. L. Pacheco, & S. Wein (Hrsg.), Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop). Albuquerque, USA: Association for Computational Linguistics. Abstract
The effectiveness of embedding methods is crucial for optimizing text classification performance in Automated Machine Learning (AutoML). However, selecting the most suitable pre-trained model for a given task remains challenging. This study introduces the Corpus-Driven Domain Mapping (CDDM) pipeline, which utilizes a domain-annotated corpus of pre-fine-tuned models from the Hugging Face Model Hub to improve model selection. Integrating these models into AutoML systems significantly boosts classification performance across multiple datasets compared to baseline methods. Despite some domain recognition inaccuracies, results demonstrate CDDM’s potential to enhance model selection, streamline AutoML workflows, and reduce computational costs. |
VerbCraft: Morphologically-aware Armenian text generation using LLMs in low-resource settings.Avetisyan, H., & Broneske, D. (2025).VerbCraft: Morphologically-aware Armenian text generation using LLMs in low-resource settings. In ¦. A. Holdt, N. Ilinykh, B. Scalvini, M. Bruton, I. N. Debess, & C. M. Tudor (Hrsg.), Proceedings of the Third Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2025) (S. 111-119). Tallinn: University of Tartu Library, Estonia. |
Tell me more! Using multiple features for binary text classification with a zero-shot model.Broneske, D., Italiya, N., & Mierisch, F. (2025).Tell me more! Using multiple features for binary text classification with a zero-shot model. In IEEE Institute of Electrical and Electronic Engineers (Hrsg.), 2024 International Conference on Machine Learning and Applications (ICMLA) (S. 1613-1620). Jacksonville, Florida, USA: IEEE Xplore. https://doi.org/10.1109/ICMLA61862.2024.00249 |
ADAMANT: Hardware-accelerated query processing made easy.Broneske, D., Burtsev, V., Drewes, A., Gurumurthy, B., Pionteck, T., & Saake, G. (2025).ADAMANT: Hardware-accelerated query processing made easy. In K.-U. Sattler, A. Kemper, T. Neumann, & J. Teubner (Hrsg.), Scalable Data Management for Future Hardware (S. 1-38). Cham: Springer. https://doi.org/10.1007/978-3-031-74097-8 |
Bots in web survey interviews: A showcase.Höhne, J. K., Claaßen, J., Shahania, S., & Broneske, D. (2025).Bots in web survey interviews: A showcase. International Journal of Market Research, 67(1), 3-12. https://doi.org/10.1177/14707853241297009 |
A mediation strategy for communication between an internal chat system and an open source chat system.Obionwu, C. V., Kanagaraj, R. R., Kalu, K. O., Broneske, D., Buch, A., Knopke, C., & Saake, G. (2024).A mediation strategy for communication between an internal chat system and an open source chat system. In Jon-Chao, H. (Hrsg.), New Technology in Education and Training, Select Proceedings of the 5th International Conference on Advance in Education and Information Technology (AEIT 2024) (S. 73-86). Singapore: Springer. https://doi.org/10.1007/978-981-97-3883-0_7 |
Exploring the predictive factors of heart disease using rare association rule mining.Darrab, S., Broneske, D., & Saake, G. (2024).Exploring the predictive factors of heart disease using rare association rule mining. Scientific Reports, 14. https://doi.org/10.1038/s41598-024-69071-6 Abstract
Cardiovascular diseases continue to be the leading cause of mortality worldwide, claiming a significant number of lives each year. Despite the advancements in predictive models, including logistic regression, neural networks, and random forests, these techniques often lack transparency and interpretability, limiting their practical application in clinical settings. To address this challenge, this research introduces EPFHD-RARMING, an innovative approach designed to enhance the understanding and predictability of heart disease through the discovery of rare and meaningful patterns. EPFHD-RARMING utilizes rare association rule mining to [...] Full Abstract: https://www.nature.com/articles/s41598-024-69071-6#citeas |
Expert agent guided learning with transformers and knowledge graphs.Obionwu, C. V., Chovatta Valappil, B. B., Genty, M., Jomy, M., Padmanabhan, V., ... & Saake, G. (2024).Expert agent guided learning with transformers and knowledge graphs. In SciTePress Science and Technology Publications (Hrsg.), Proceedings of the 13th International Conference on Data Science, Technology and Applications (DATA 2024) (S. 180-189). Setúbal, Portugal: Science and Technology Publications. |
Sharing software-evolution datasets: Practices, challenges, and recommendations.Broneske, D., Kittan, S., & Krüger, J. (2024).Sharing software-evolution datasets: Practices, challenges, and recommendations. In Association for Computing Machinery (Hrsg.), Proceedings of the ACM on Software Engineering (S. 2051-2074). New York, NY, United States: ACM. https://doi.org/10.1145/3660798 |
Exploiting shared sub-expression and materialized view reuse for multi-query optimization.Gurumurthy, B., Bidarkar, V. R., Broneske, D., Pionteck, T., & Saake, G. (2024).Exploiting shared sub-expression and materialized view reuse for multi-query optimization. Information Systems Frontiers, A Journal of Research and Innovation. https://doi.org/10.1007/s10796-024-10506-w |
A design proposal for a unified B-epsilon-tree: Embracing NVM in memory hierarchies.Karim, S., Wünsche, J., Broneske, D., Kuhn, M., & Saake, G. (2024).A design proposal for a unified B-epsilon-tree: Embracing NVM in memory hierarchies. In Störl, U. (Hrsg.), GvDB 2024, Grundlagen von Datenbanken 2024, Proceedings of the 35th GI-Workshop Grundlagen von Datenbanken (Herdecke, Germany, May 22-24, 2024) (S. 43-50). Hagen: Fernuniversität Hagen, Databases and Information Systems. |
A case study on the development of the German Corona-Warn-App.Fawaz Enaya, M., Klingbeil, T., Krüger, J., Broneske, D., Feinbube, F., & Saake, G. (2024).A case study on the development of the German Corona-Warn-App. Journal of Systems and Software. https://doi.org/10.1016/j.jss.2024.112020 |
A domain specific students' assistance system for the provision of instructional feedback.Obionwu, C. V., Tiwari, T., Chovatta Valappil, B. B., Raikar, N., Walia, D. S., ... & Saake, G. (2024).A domain specific students' assistance system for the provision of instructional feedback. In IEEE Institute of Electrical and Electronic Engineers (Hrsg.), 2023 International Conference on Machine Learning and Applications (ICMLA) (S. 2065-2070). Jacksonville, Florida, USA: IEEE. https://doi.org/10.1109/ICMLA58977.2023.00312 |
Presentations
List of presentations & conferences
Curriculum Vitae
Since 03/2021
Acting Head of Department 4 "Infrastructures and Methods" of the German Centre for Higher Education Research and Science Studies (DZHW)
04/2020 - 02/2021
Post-Doc at Otto-von-Guericke University Magdeburg
10/2019 - 03/2020
Substitutional Professor at Hochschule Anhalt - Chair of Database and Information Systems
06/2019 - 09/2019
Post-Doc at Otto-von-Guericke University Magdeburg
08/2013 - 05/2019
PhD Student at Otto-von-Guericke University Magdeburg
2008 - 2013
Bachelor and Master Studies in "Computer Science", Otto-von-Guericke University Magdeburg
