Dr. David Broneske studierte Informatik im Bachelor und Master an der Otto-von-Guericke Universität Magdeburg, wo er 2019 promovierte und darauf seine Habilitation begann. Von 2019 bis 2020 übernahm er die Vertretung des Lehrstuhls Datenbank und Informationssysteme an der Hochschule Anhalt in Köthen. Er hat im März 2021 die kommissarische Leitung der Abteilung 4 "Infrastrukturen und Methoden" des Deutschen Zentrums für Hochschul- und Wissenschaftsforschung (DZHW) übernommen.
Dr. David Broneske
Abteilung Infrastruktur und Methoden
Kommissarische Abteilungsleitung
- broneske@dzhw.eu
- 0511 450670-454
- Google Scholar
- Orcid
Wissenschaftliche Forschungsgebiete
Forschungsdatenmanagement für Learning Analytics Daten, Hauptspeicherdatenbanken auf moderner Hardware, Interaktive Datenexploration und Visualisierung, Künstliche Intelligenz für Datenbereinigung und Datenanalyse
Projekte
Publikationen
Vorträge & Tagungen
CV
Projekte
Publikationen
Liste der Publikationen
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 |
Knowledge distillation for quantized vehicle sensor data.Vox, C., Niemann, O., Broneske, D., Piewek, J., Sass, A. U., & Saake, G. (2024).Knowledge distillation for quantized vehicle sensor data. In IEEE Institute of Electrical and Electronic Engineers (Hrsg.), 2023 International Conference on Machine Learning and Applications (ICMLA) (S. 908-915). Jacksonville, Florida, USA: IEEE. https://doi.org/10.1109/ICMLA58977.2023.00134 |
Enforcing right to be forgotten in cloud-based data lakes.Bhardwaj, P., Darrab, S., Broneske, D., Klose, I., & Saake, G. (2024).Enforcing right to be forgotten in cloud-based data lakes. In Arai, K. (Hrsg.), Advances in Information and Communication (FICC 2024) (S. 220-234). Cham: Springer. https://doi.org/10.1007/978-3-031-53963-3_16 |
An evolutionary algorithm with heuristic operator for detecting protein complexes in protein interaction networks with negative controls.Abbas, M. N., Attea, B. A., Broneske, D., & Saake, G. (2024).An evolutionary algorithm with heuristic operator for detecting protein complexes in protein interaction networks with negative controls. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3367746 Abstract
Computational biology research faces a formidable challenge in the detection of complexes within protein-protein interaction (PPI) networks, critical for unraveling cellular processes, predicting functions of uncharacterized proteins, and diagnosing diseases. While evolutionary algorithms (EAs), particularly state-of-the-art methods, often partition PPI networks based on graph properties or biological semantics, their resilience to noisy or missing interactions remains an underexplored territory. In this paper, we propose a groundbreaking heuristic operator, termed "strong neighbor-node migration", specifically designed to elevate solution quality [...] Full Abstract: https://ieeexplore.ieee.org/document/10440281 |
Framing and BERTology: A data-centric approach to integration of linguistic features into transformer-based pre-trained language models.Avetisyan, H., Safikhani, P., & Broneske, D. (2024).Framing and BERTology: A data-centric approach to integration of linguistic features into transformer-based pre-trained language models. In Arai, K. (Hrsg.), Intelligent Systems and Applications (S. 81-90). Cham: Springer. https://doi.org/10.1007/978-3-031-47718-8 |
Clustering graph data: the roadmap to spectral techniques.Mondal, R., Ignatova, E., Walke, D., Broneske, D., Saake, G., & Heyer, R. (2024).Clustering graph data: the roadmap to spectral techniques. Discover Artificial Intelligence, 4(7), 1-22. https://doi.org/10.1007/s44163-024-00102-x Abstract
Graph data models enable efficient storage, visualization, and analysis of highly interlinked data, by providing the benefits of horizontal scalability and high query performance. Clustering techniques, such as K-means, hierarchical clustering, are highly beneficial tools in data mining and machine learning to find meaningful similarities and differences between data points. Recent developments in graph data models, as well as clustering algorithms for graph data, have shown promising results in image segmentation, gene data analysis, etc. This has been primarily achieved through research and development of algorithms in the field of spectral theory, [...] Full abstract: https://doi.org/10.1007/s44163-024-00102-x |
Anomaly detection algorithms: Comparative analysis and explainability perspectives.Darab, S., Allipilli, H., Ghani, S., Changaramkulath, H., Koneru, S., Broneske, D., & Saake, G. (2023).Anomaly detection algorithms: Comparative analysis and explainability perspectives. In D. Benavides-Prado et al. (Hrsg.), Data Science and Machine Learning, 21st Australasian Conference, AusDM 2023, Auckland, New Zealand, December 11–13, 2023, Proceedings (S. 90-104). Singapore: Springer Nature. |
Vorträge & Tagungen
Liste der Vorträge & Tagungen
CV
Seit 03/2021
Kommissarischer Leiter der Abteilung 4 "Infrastruktur und Methoden" am Deutschen Zentrum für Hochschul- und Wissenschaftsforschung (DZHW)
04/2020 - 02/2021
Post-Doc an der Otto-von-Guericke-Universität Magdeburg - Mitarbeiter in Lehre und Forschung
10/2019 - 03/2020
Vertretungsprofessur an der Hochschule Anhalt - Lehrstuhl Datenbank und Informationssysteme
06/2019 - 09/2019
Post-Doc an der Otto-von-Guericke-Universität Magdeburg - Mitarbeiter in Lehre und Forschung
08/2013 - 05/2019
Doktorand an der Otto-von-Guericke-Universität Magdeburg - Mitarbeiter in Lehre und Forschung
2008 - 2013
Bachelorstudium und Masterstudium "Informatik", Otto-von-Guericke-Universität Magdeburg
Lehre
- SoSe 2016, 2020 & 2021 Advanced Topics in Databases (OVGU; ca. 50 Teilnehmer) - Vorlesung
- WiSe 2020 Data Warehouse Technologies (OVGU; ca. 80 Teilnehmer) - Vorlesung & Übung
- WiSe 2020 Datenbanken I (OVGU; ca. 270 Teilnehmer) - Vorlesung & Übung
- SoSe 2013 & 2020 Datenbanken Implementierungstechniken (OVGU; ca. 40 Teilnehmer) - Übung
- WiSe 2019 Moderne Datenbankkonzepte (HS-Anhalt; ca. 12 Teilnehmer) - Vorlesung & Übung
- WiSe 2019 Datenbanksysteme (HS-Anhalt; ca. 40 Teilnehmer ) - Vorlesung & Übung
- SoSe 2015-2019 Database Concepts (OVGU; ca. 120 Teilnehmer) - Übung
- WiSe 2012-2018 Datenbanken I (OVGU, ca. 270 Teilnehmer) - Übung
- SoSe 2012-2014 Datenmanagement (OVGU; ca. 100 Teilnehmer) - Übung
Auszeichnungen
- 2020 - Distinguished Reviewer at Information Systems, Elsevier
- 2017 - Forschungspreis der Fakultät für Informatik der Otto-von-Guericke-Universität Magdeburg
