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David Broneske

Dr. David Broneske

Abteilung Infrastruktur und Methoden
Kommissarische Abteilungsleitung
  • 0511 450670-454
  • 0511 450670-960
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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.

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Wissenschaftliche Forschungsgebiete

Forschungsdatenmanagement für Learning Analytics Daten, Hauptspeicherdatenbanken auf moderner Hardware, Interaktive Datenexploration und Visualisierung, Künstliche Intelligenz für Datenbereinigung und Datenanalyse

Publikationen

Liste der Publikationen

Leider konnte für diese Suchkombination kein Ergebnis gefunden werden

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

Laughing out loud – Exploring AI-generated and human-generated humor.

Safikhani, P., Avetisyan, H., & Broneske, D. (2023).
Laughing out loud – Exploring AI-generated and human-generated humor. Computer Science & Information Technology (CS & IT), 2023, 59-76.

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.

WISHFUL - Website extraction of Institutional Sources with Heterogeneous Factors and User-Driven Linkage.

Shahania, S., Spiliopoulou, M., & Broneske, D. (2023).
WISHFUL - Website extraction of Institutional Sources with Heterogeneous Factors and User-Driven Linkage. In Delir Haghighi, P. et al. (Hrsg.), Information Integration and Web Intelligence (iiWAS 2023) (S. 20-26). Cham: Springer. https://doi.org/10.1007/978-3-031-48316-5_3
Abstract

Extracting information from diverse websites is increasingly important, especially for analyzing vast data sets to detect trends, gain insights. By studying job ads, researchers can monitor employer demand shifts, assisting policymakers in aiding affected workers and industries. However, extraction faces challenges like varied website formats, dynamic content, and duplicate data. This study introduces a method for extracting data from diverse private university websites involving keyword identification, website categorization, and extraction pipelines.

Optical image recognition strategy for keyword extraction and page ranking for slide recommendation system.

Obionwu, C. V., Abbas, S. M. L., Padmanabhan, V., Tiwari, T., Broneske, D., & Saake, G. (2023).
Optical image recognition strategy for keyword extraction and page ranking for slide recommendation system. In IEEE Institute of Electrical and Electronic Engineers (Hrsg.), 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) (S. 1-6). Tenerife, Spain: IEEE. https://doi.org/10.1109/ICECCME57830.2023.10252423

A strategy for retrospective evaluation of students SQL learning engagements.

Obionwu, C. V., Kalu, K. O., Blockhaus, P., Broneske, D., & Saake, G. (2023).
A strategy for retrospective evaluation of students SQL learning engagements. In IEEE Institute of Electrical and Electronic Engineers (Hrsg.), 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME 2023) (S. 1-7). Tenerife, Spain: IEEE. https://doi.org/10.1109/ICECCME57830.2023.10252347

Decoding the encoded - Linguistic secrets of language models: A systematic literature review.

Avetisyan, H., & Broneske, D. (2023).
Decoding the encoded - Linguistic secrets of language models: A systematic literature review. In D. C. Wyld & D. Nagamalai (Hrsg.), Computer Science & Information Technology (CS & IT) (S. 67-88). Chennai, Tamil Nadu, India: AIRCC Publishing Corporation.

FPGA-integrated bag of little bootstraps accelerator for approximate database query processing.

Burtsev, V., Wilhelm, M., Drewes, A., Gurumurthy, B., Broneske, D., Pionteck, T., & Saake, G. (2023).
FPGA-integrated bag of little bootstraps accelerator for approximate database query processing. In F. Palumbo, G. Keramidas, N. Voros, & P. C. Diniz (Hrsg.), Applied Reconfigurable Computing. Architectures, Tools, and Applications (ARC 2023). Lecture Notes in Computer Science (S. 115-130). Cham: Springer.

Enhancing AutoNLP with fine-tuned BERT models: An evaluation of text representation methods for AutoPyTorch.

Safikhani, P., & Broneske, D. (2023).
Enhancing AutoNLP with fine-tuned BERT models: An evaluation of text representation methods for AutoPyTorch. In D. C. Wyld & D. Nagamalai (Hrsg.), Computer Science & Information Technology (CS & IT) (S. 23-38). Chennai, Tamil Nadu, India: AIRCC Publishing Corporation.

What happens when two multi-query optimization paradigms combine?

Gurumurthy, B., Bidarkar, V. R., Broneske, D., Pionteck, T., & Saake, G. (2023).
What happens when two multi-query optimization paradigms combine? In A. Abelló, P. Vassiliadis, O. Romero, & R. Wrembel (Hrsg.), Advances in Databases and Information Systems (ADBIS 2023). Cham: Springer (online first). https://doi.org/10.1007/978-3-031-42914-9_6

Investigating lakehouse-backbones for vehicle sensor data.

Vox, C., Broneske, D., Piewek, J., Feigel, J., & Saake, G. (2023).
Investigating lakehouse-backbones for vehicle sensor data. In C. Strauss, T. Amagasa, G. Kotsis, A. M. Tjoa, & I. Khalil (Hrsg.), Database and Expert Systems Applications (DEXA 2023). Cham: Springer (online first). https://doi.org/10.1007/978-3-031-39847-6_17

Automatic instructional feedback, and a lecture hub system: A strategy towards nurturing the acquisition of a structured engagement behavior.

Obionwu, V., Toulouse, V., Broneske, D., & Saake, G. (2023).
Automatic instructional feedback, and a lecture hub system: A strategy towards nurturing the acquisition of a structured engagement behavior. In A. Cuzzocrea, O. Gusikhin, S. Hammoudi, & C. Quix (Hrsg.), Data Management Technologies and Applications. DATA DATA 2022 2021. Communications in Computer and Information Science. Cham: Springer. https://doi.org/10.1007/978-3-031-37890-4_11

AUTARKYO: Interactive app for the planning and evaluation of energy self-sufficient houses.

Erhardt, A.-L., Rzepus, H. J., Griel, C., Broneske, D., & Vorhauer-Huget, N. (2023).
AUTARKYO: Interactive app for the planning and evaluation of energy self-sufficient houses. In IATED (Hrsg.), EDULEARN23 Proceedings of the 15th International Conference on Education and New Learning Technologies, Palma de Mallorca, Spain. 3-5 July, 2023 (S. 1458-1467). Valencia, Spain: IATED Academy. https://doi.org/10.21125/edulearn.2023.0453
Vorträge & Tagungen

Liste der Vorträge & Tagungen

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Laughing out loud – Exploring AI-generated and human-generated humor.

Avetisyan, H., Safikhani, P., & Broneske, D. (2023, Dezember).
Laughing out loud – Exploring AI-generated and human-generated humor. Vortrag auf der Konferenz International Conference on NLP & Artificial Intelligence Techniques (NLAI 2023), Computer Science & Information Technology (CS & IT), Sydney, Australia.

Anomaly detection algorithms: Comparative analysis and explainability perspectives.

Darab, S., Allipilli, H., Ghani, S., Changaramkulath, H., Koneru, S., Broneske, D., & Saake, G. (2023, Dezember).
Anomaly detection algorithms: Comparative analysis and explainability perspectives. Vortrag auf der Konferenz The 21st Australasian Data Science and Machine Learning Conference (AUSDM23), Auckland, New Zealand.

WISHFUL - Website Extraction of Institutional Sources with Heterogeneous Factors and User-Driven Linkage.

Shahania, S., Spiliopoulou, M., & Broneske, D. (2023, Dezember).
WISHFUL - Website Extraction of Institutional Sources with Heterogeneous Factors and User-Driven Linkage. Vortrag auf der Konferenz The 25th International Conference on Information Integration and Web Intelligence (iiWAS 2023) and The 21st International Conference on Advances in Mobile Computing & Multimedia Intelligence (MoMM2023), Denpasar, Bali, Indonesien.

FPGA-integrated bag of little bootstraps accelerator for approximate database query processing.

Burtsev, V., Wilhelm, M., Drewes, A., Gurumurthy, B., Broneske, D., Pionteck, T., & Saake, G. (2023, September).
FPGA-integrated bag of little bootstraps accelerator for approximate database query processing. Vortrag im Rahmen des 19th International Symposium on Applied Reconfigurable Computing (ARC 2023), Cottbus.

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. (2023, September).
Framing and BERTology: ​A data-centric approach to integration of linguistic features into transformer-based pre-trained language models​. Vortrag auf der Konferenz Intelligent Systems Conference (IntelliSys 2023), Amsterdam, The Netherlands.

Optical image recognition strategy for keyword extraction and page ranking for slide recommendation system.

Obionwu, C. V., Abbas, S. M. L., Padmanabhan, V., Tiwari, T., Broneske, D., & Saake, G. (2023, Juli).
Optical image recognition strategy for keyword extraction and page ranking for slide recommendation system. Vortrag auf der Konferenz 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME 2023), Teneriffa, Spanien.

A strategy for retrospective evaluation of students SQL learning engagements.

Obionwu, C. V., Kalu, K. O., Blockhaus, P., Broneske, D., & Saake, G. (2023, Juli).
A strategy for retrospective evaluation of students SQL learning engagements. Vortrag auf der Konferenz 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME 2023), Teneriffa, Spanien. https://doi.org/10.1109/ICECCME57830.2023.10252347

A strategy for structuring teams collaboration in university course projects.

Obionwu, C. V., Karl, M., Broneske, D., Hawlitschek, A., Blockhaus, P., & Saake, G. (2023, Juli).
A strategy for structuring teams collaboration in university course projects. Vortrag auf der Konferenz 20th International Conference on Smart Business Technologies (ICSBT 2023), Rom, Italien.

What happens when two multi-query optimization paradigms combine?

Gurumurthy, B., Bidarkar, V. R., Broneske, D., Pionteck, T., & Saake, G. (2023, Juli).
What happens when two multi-query optimization paradigms combine? Vortrag auf der Konferenz European Conference on Advances in Databases and Information Systems (ADBIS 2023), Barcelona, Spanien.

Towards a future of fully self-optimizing query engines.

Blockhaus, P., Campero Durand, G., Broneske, D., & Saake, G. (2023, Juni).
Towards a future of fully self-optimizing query engines. Vortrag im Rahmen des 34th Workshop on Basics of Database Systems (Grundlagen von Datenbanken GVDB23), Calw.

Vertical vectorized hashing for faster group-by aggregation.

Nijalingappa, S., Gurumurthy, B., Broneske, D., & Saake, G. (2023, April).
Vertical vectorized hashing for faster group-by aggregation. Vortrag auf dem Workshop Joint International Workshop on Big Data Management on Emerging Hardware and Data Management on Virtualized Active Systems (HardBD & Active'23), Anaheim, California, USA.

Klausurtagung des DZHW-Forschungsclusters " Open Science " .

Blümel, C., Broneske, D., Daniel, A., Hartstein, J., Schniedermann, A., & Velden, T. (2023, März).
Workshop Klausurtagung des DZHW-Forschungsclusters "Open Science", DZHW, Berlin.

Second Workshop on Novel Data Management Ideas on Heterogeneous (Co-)Processors (NoDMC).

Broneske, D., & Habich, D. (2023, März).
Workshop Second Workshop on Novel Data Management Ideas on Heterogeneous (Co-)Processors (NoDMC) im Rahmen der 20th Conference on Database Systems for Business, Technology and Web (BTW 2023), TU Dresden, Dresden.

Data streams: Investigating data structures for multivariate asynchronous time series prediction problems.

Vox, C., Broneske, D., Shaikat, I., & Saake, G. (2023, Februar).
Data streams: Investigating data structures for multivariate asynchronous time series prediction problems. Vortrag auf der Konferenz 12th International Conference on Pattern Recognition Applications and Methods (ICPRAM), Lissabon, Portugal.

An intervention strategy for mitigating the prevalence of syntax errors during task exercise engagements.

Obionwu, V., Kumar, R., Shantaram, S., Broneske, D., & Saake, G. (2023, Januar).
An intervention strategy for mitigating the prevalence of syntax errors during task exercise engagements. Vortrag auf der Konferenz 6th World Conference on Computing and Communication Technologies (WCCCT), IEEE, Chengdu, China.
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

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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