Institute of Information Systems
Organisational unit: Institute
- Junior professorship for Information Systems, in particular Data Analytics
- Professorship for Computer Engineering
- Professorship for Information Systems, in particular Artificial Intelligence and Explainability
- Professorship for Information Systems, in particular Data Science
- Professorship for Information Systems, in particular Digital Transformation and Information Management
- Professorship for Information Systems, in particular E-Business and Knowledge Management
- Professorship for Information Systems, in particular Machine Learning
- Professorship for Information Systems, in particular Network Science
Organisation profile
At the Institute of Information Systems (IIS) of the Leuphana University we work on innovative topics in Information Systems and Data Science. The main focus is on digital transformation, e-health, e-commerce, business analytics, sports and e-learning. Methodologically, we focus on the development and use of machine learning and artificial intelligence methods for the modeling and solution of data-driven decision problems. Additional quantitative (e.g. graphs, optimization) and qualitative (e.g. interviews, reference modeling) methods complement this spectrum. Cooperating with other universities, companies, and institutions of the civil society plays an important role in our work.
Main research areas
At the Institute of Information Systems there are currently 6 professors and numerous research assistants. Our main research areas include digital transformation, e-health, e-commerce, business analytics, sports, and e-learning. Details can be found on the websites of the work groups and in the central research database of Leuphana. The Institute of Information Systems organized a number of events and conferences at Leuphana (e.g. ITEE 2013, final round of the German National Computer Science Competition 2014, MKWI 2018).
- Published
Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data
Hoogendoorn, M. & Funk, B., 2018, 1 ed. Cham: Springer International Publishing. 231 p. (Cognitive Systems Monograph; vol. 35)Research output: Books and anthologies › Monographs › Research › peer-review
- Published
Predicting Therapy Success For Treatment as Usual and Blended Treatment in the Domain of Depression
van Breda, W. R. J., Bremer, V., Becker, D., Funk, B., Ruwaard, J. & Riper, H., 01.06.2018, In: Internet Interventions. 12, p. 100-104 5 p.Research output: Journal contributions › Journal articles › Research › peer-review
- Published
Bimodal Enterprise Architecture Management: The emergence of a new EAM function for a BizDevOps-based fast IT
Drews, P., Schirmer, I., Horlach, B. & Tekaat, C., 02.11.2017, Proceedings - 2017 IEEE 21st International Enterprise Distributed Object Computing Conference Workshops, EDOCW 2017: 10–13 October 2017 Quebec City, Quebec, Canada, Proceedings. Lapalme, J., Halle, S. & Dijkman, R. (eds.). IEEE - Institute of Electrical and Electronics Engineers Inc., p. 57-64 8 p. (Proceedings - IEEE International Enterprise Distributed Object Computing Workshop, EDOCW; vol. 2017-October).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
- Published
Guest editorial: Special issue on sports analytics
Brefeld, U. & Zimmermann, A., 01.11.2017, In: Data Mining and Knowledge Discovery. 31, 6, p. 1577-1579 3 p.Research output: Journal contributions › Other (editorial matter etc.) › Research
- Published
Frame-based Data Factorizations
Mair, S., Boubekki, A. & Brefeld, U., 25.07.2017, 34th International Conference on Machine Learning, ICML 2017. Precup, D. & Teh, Y. W. (eds.). Red Hook: Curran Associates, p. 2305-2313 9 p. (Proceedings of Machine Learning Research; vol. 70).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
- Published
Distributed robust Gaussian Process regression
Mair, S. & Brefeld, U., 01.05.2018, In: Knowledge and Information Systems. 55, 2, p. 415-435 21 p.Research output: Journal contributions › Journal articles › Research › peer-review
- Published
A Unified Contextual Bandit Framework for Long- and Short-Term Recommendations
Tavakol, M. & Brefeld, U., 30.12.2017, Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017 Skopje, Macedonia, September 18 – 22, 2017; Proceedings, Part II. Ceci, M., Hollmen, J., Todorovski, L., Vens, C. & Dzeroski, S. (eds.). Springer Verlag, Vol. 2. p. 269-284 16 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 10535 LNAI).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
- Published
Metaheuristics approach for solving personalized crew rostering problem in public bus transit
Xie, L., Merschformann, M., Kliewer, N. & Suhl, L., 10.2017, In: Journal of Heuristics. 23, 5, p. 321-347 27 p.Research output: Journal contributions › Journal articles › Research › peer-review
- Published
Digitalization: Opportunity and Challenge for the Business and Information Systems Engineering Community
Legner, C., Eymann, T., Hess, T., Matt, C., Böhmann, T., Drews, P., Mädche, A., Urbach, N. & Ahlemann, F., 01.08.2017, In: Business and Information Systems Engineering. 59, 4, p. 301-308 8 p.Research output: Journal contributions › Journal articles › Research › peer-review
- Published
Simulated annealing approach to nurse rostering benchmark and real-world instances
Knust, F. & Xie, L., 01.01.2019, In: Annals of Operations Research. 272, 1-2, p. 187-216 30 p.Research output: Journal contributions › Journal articles › Research › peer-review