For bachelor students we offer German lectures on database systems in addition to paper- or project-oriented seminars. Within a one-year bachelor project, students finalize their studies in cooperation with external partners. For master students we offer courses on information integration, data profiling, and information retrieval enhanced by specialized seminars, master projects and we advise master theses.
Most of our research is conducted in the context of larger research projects, in collaboration across students, across groups, and across universities. We strive to make available most of our datasets and source code.
CAST: Classifying Time Series Anomalies (2022/2023)
UltraMine - Scalable Analytics on Time Series Data (2020/2021)
Large-Scale Time Series Analytics (Master, 2021/2022)
Sustainable Machine Learning on Edge Device Clusters (Master, 2020, assistance)
Guest Lecture about distributed discovery of Order Dependencies for the Data Profiling course (2020/2021)
Master Thesis (supervision and assistance):
Correlation Anomaly Detection in High-Dimensional Time Series (Niklas Köhnecke, ongoing)
HYPEX: Explainable Hyperparameter Optimization in Time Series Anomaly Detection (Mats Pörschke, 2022)
Time Series Anomaly Detection: An Aircraft Turbine Case Study (Jacopo Roberto Nicosia, 2022)
A2DB: A Reactive Database for Theta-Joins (Julian Weise, 2020)
Marcian Seeger, Sebastian Schmidl, Alexander Vielhauer, Thorsten Papenbrock: DPQL: The Data Profiling Query Language. Proceedings of the conference on Database Systems for Business, Technology, and Web (BTW), 2023 (to appear)
Sebastian Schmidl, Phillip Wenig, Thorsten Papenbrock: HYPEX: Hyperparameter Optimization in Time Series Anomaly Detection. Proceedings of the conference on Database Systems for Business, Technology, and Web (BTW), 2023 (to appear)
Sebastian Schmidl, Frederic Schneider, Thorsten Papenbrock: An Actor Database System for Akka. Proceedings of the conference on Database Systems for Business, Technology, and Web (BTW) - Workshopband, 2019 [Paper][DOI:10.18420/btw2019-ws-23]