@article{LamprechtMargaria2015,
  author    = {Lamprecht, Anna-Lena and Margaria, Tiziana},
  title     = {Scientific workflows and XMDD},
  series = {Process design for natural scientists},
  journal   = {Process design for natural scientists},
  editor    = {Lamprecht, Anna-Lena and Margaria, Tiziana},
  publisher = {Springer},
  address   = {Berlin},
  isbn      = {978-3-662-45006-2},
  pages     = {1 -- 13},
  year      = {2015},
  language  = {en}
}
@article{GruenewaldMeinel2015,
  author    = {Gr{\"u}newald, Franka and Meinel, Christoph},
  title     = {Implementation and Evaluation of Digital E-Lecture Annotation in Learning Groups to Foster Active Learning},
  series = {IEEE transactions on learning technologies},
  volume    = {8},
  journal   = {IEEE transactions on learning technologies},
  number    = {3},
  publisher = {Inst. of Electr. and Electronics Engineers},
  address   = {Los Alamitos},
  issn      = {1939-1382},
  doi       = {10.1109/TLT.2015.2396042},
  pages     = {286 -- 298},
  year      = {2015},
  abstract  = {The use of video lectures in distance learning involves the two major problems of searchability and active user participation. In this paper, we promote the implementation and usage of a collaborative educational video annotation functionality to overcome these two challenges. Different use cases and requirements, as well as details of the implementation, are explained. Furthermore, we suggest more improvements to foster a culture of participation and an algorithm for the extraction of semantic data. Finally, evaluations in the form of user tests and questionnaires in a MOOC setting are presented. The results of the evaluation are promising, as they indicate not only that students perceive it as useful, but also that the learning effectiveness increases. The combination of personal lecture video annotations with a semantic topic map was also evaluated positively and will thus be investigated further, as will the implementation in a MOOC context.},
  language  = {en}
}
@phdthesis{SadrAzodi2015,
  author    = {Sadr-Azodi, Amir Shahab},
  title     = {Towards Real-time SIEM-based Network monitoring and Intrusion Detection through Advanced Event Normalization},
  school      = {Universit{\"a}t Potsdam},
  pages     = {144},
  year      = {2015},
  language  = {en}
}
@article{OmranianMuellerRoeberNikoloski2015,
  author    = {Omranian, Nooshin and M{\"u}ller-R{\"o}ber, Bernd and Nikoloski, Zoran},
  title     = {Segmentation of biological multivariate time-series data},
  series = {Scientific reports},
  volume    = {5},
  journal   = {Scientific reports},
  publisher = {Nature Publ. Group},
  address   = {London},
  issn      = {2045-2322},
  doi       = {10.1038/srep08937},
  pages     = {6},
  year      = {2015},
  abstract  = {Time-series data from multicomponent systems capture the dynamics of the ongoing processes and reflect the interactions between the components. The progression of processes in such systems usually involves check-points and events at which the relationships between the components are altered in response to stimuli. Detecting these events together with the implicated components can help understand the temporal aspects of complex biological systems. Here we propose a regularized regression-based approach for identifying breakpoints and corresponding segments from multivariate time-series data. In combination with techniques from clustering, the approach also allows estimating the significance of the determined breakpoints as well as the key components implicated in the emergence of the breakpoints. Comparative analysis with the existing alternatives demonstrates the power of the approach to identify biologically meaningful breakpoints in diverse time-resolved transcriptomics data sets from the yeast Saccharomyces cerevisiae and the diatom Thalassiosira pseudonana.},
  language  = {en}
}