Mourad Khayati is a Senior researcher at the Department of Computer Science of the University of Fribourg, Switzerland. He did his PhD at University of Zurich, Switzerland, under the supervision of Prof. Michael Böhlen. He is leading the technical committee of the H2020 European project FashionBrain and is the principal investigator of the D-A-CH ProvDS project.
Time Series, recovery of missing values, and matrix decomposition techniques.
- DBLP server.
- Internal Server:
- Laura Rettig, Mourad Khayati, Philippe Cudré-Mauroux, and Michal Piorkowski. “Online Anomaly Detection over Big Data Streams.” In 2015 IEEE International Conference on Big Data, Big Data 2015, Santa Clara, CA, USA, Oct 27- Nov 01, 2015. Bibtex PDF
- Mourad Khayati, Philippe Cudré-Mauroux, and Michael H. Böhlen. “Using Lowly Correlated Time Series to Recover Missing Values in Time Series: a Comparison between SVD and CD.” In Advances in Spatial and Temporal Databases - 15th International Symposium, SSTD 2015, Seoul, South Korea, August 26-28, 2015. Proceedings, 2015. Bibtex PDF
- Mourad Khayati, Michael H. Böhlen, and Johann Gamper. “Memory-Efficient Centroid Decomposition for Long Time Series.” In IEEE 30th International Conference on Data Engineering, Chicago, ICDE 2014, IL, USA, March 31 - April 4, 2014, 100–111, 2014. Bibtex PDF
- Mourad Khayati, and Michael H. Böhlen. “REBOM: Recovery of Blocks of Missing Values in Time Series.” In Proceedings of the 18th International Conference on Management of Data, COMAD 2012, 2012, Pune, India, 44–55, 2012. Bibtex PDF
- PhD Thesis:
Mourad Khayati. Recovery of Missing Values using Matrix Decomposition Techniques, University of Zurich, Switzerland. PDF
- FashionBrain (H2020 European project):
- Role: Technical lead
- Period: 1.1.2017-31.12.2019
- Total cost: 2.9M EUR
- ProvDS (D-A-CH project):
- Role: Principal investigator
- Period: 1.1.2018-31.12.2020
- Total cost: 820K EUR
- c-ReviVal: Real-time Centroid Decomposition for streams
- Ongoing theses:
- Old theses: