@article{WahlHoelscher2018,
  author    = {Wahl, Marina and H{\"o}lscher, Michael},
  title     = {Und am Wochenende Blended Learning},
  series = {E-Learning Symposium 2018},
  journal   = {E-Learning Symposium 2018},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  doi       = {10.25932/publishup-42191},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-421910},
  pages     = {17 -- 27},
  year      = {2018},
  abstract  = {Berufsbegleitende Studieng{\"a}nge stehen vor besonderen Schwierigkeiten, f{\"u}r die der Einsatz von Blended Learning-Szenarien sinnvoll sein kann. Welche speziellen Herausforderungen sich dabei ergeben und welche L{\"o}sungsans{\"a}tze dagegen steuern, betrachtet der folgende Artikel anhand eines Praxisberichts aus dem Studiengang M. P. A. Wissenschaftsmanagement an der Universit{\"a}t Speyer.},
  language  = {de}
}
@misc{NeubauerHaubeltWankoetal.2018,
  author    = {Neubauer, Kai and Haubelt, Christian and Wanko, Philipp and Schaub, Torsten},
  title     = {Utilizing quad-trees for efficient design space exploration with partial assignment evaluation},
  series = {2018 23rd Asia and South Pacific Design Automation Conference (ASP-DAC)},
  journal   = {2018 23rd Asia and South Pacific Design Automation Conference (ASP-DAC)},
  publisher = {IEEE},
  address   = {New York},
  isbn      = {978-1-5090-0602-1},
  issn      = {2153-6961},
  doi       = {10.1109/ASPDAC.2018.8297362},
  pages     = {434 -- 439},
  year      = {2018},
  abstract  = {Recently, it has been shown that constraint-based symbolic solving techniques offer an efficient way for deciding binding and routing options in order to obtain a feasible system level implementation. In combination with various background theories, a feasibility analysis of the resulting system may already be performed on partial solutions. That is, infeasible subsets of mapping and routing options can be pruned early in the decision process, which fastens the solving accordingly. However, allowing a proper design space exploration including multi-objective optimization also requires an efficient structure for storing and managing non-dominated solutions. In this work, we propose and study the usage of the Quad-Tree data structure in the context of partial assignment evaluation during system synthesis. Out experiments show that unnecessary dominance checks can be avoided, which indicates a preference of Quad-Trees over a commonly used list-based implementation for large combinatorial optimization problems.},
  language  = {en}
}