For bachelor students we offer German lectures on database systems in addition with 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, search engines and information retrieval enhanced by specialized seminars, master projects and advised 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 data sets and source code.
Abdul-Aziz, Ali & Woike, Mark & Oza, Nikunj & Matthews, Bryan & Lekki, John. (2011). Rotor health monitoring combining spin tests and data-driven anomaly detection methods. Structural Health Monitoring. 11. 3-12. 10.1177/1475921710395811.
Goldberger, Ary & Amaral, Luís & Glass, L. & Havlin, Shlomo & Hausdorg, J. & Ivanov, Plamen & Mark, R. & Mietus, J. & Moody, G. & Peng, Chung-Kang & Stanley, H. & Physiobank, Physiotoolkit. (2000). Components of a new research resource for complex physiologic signals. PhysioNet. 101.
Moody, G.B. & Mark, R.G.. (2001). The impact of the MIT-BIH arrhythmia database. IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society. 20. 45-50. 10.1109/51.932724.
Keogh, E. & Lin, J. & Fu, Ada. (2005). HOT SAX: Efficiently finding the most unusual time series subsequence. Proceedings - IEEE International Conference on Data Mining, ICDM. 8 pp.-. 10.1109/ICDM.2005.79.
Senin, Pavel & Lin, Jessica & Wang, Xing & Oates, Tim & Gandhi, Sunil & Boedihardjo, Arnold & Chen, Crystal & Frankenstein, Susan. (2015). Time series anomaly discovery with grammar-based compression.
Wijk, Jarke & Selow, E.. (1999). Cluster and calendar based visualization of time series data. 4-9, 140. 10.1109/INFVIS.1999.801851.
Boniol, Paul & Palpanas, Themis. (2020). Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series. Proceedings of the International Conference on Very Large Databases.