@techreport{BaumgartBredebachHermetal.2022,
  author    = {Baumgart, Michael and Bredebach, Patrick and Herm, Lukas-Valentin and Hock, David and Hofmann, Adrian and Janiesch, Christian and Jankowski, Leif Ole and Kampik, Timotheus and Keil, Matthias and Kolb, Julian and Kr{\"o}hn, Michael and Pytel, Norman and Schaschek, Myriam and St{\"u}bs, Oliver and Winkelmann, Axel and Zeiß, Christian},
  title     = {Plattform f{\"u}r das integrierte Management von Kollaborationen in Wertsch{\"o}pfungsnetzwerken (PIMKoWe)},
  editor    = {Winkelmann, Axel and Janiesch, Christian},
  issn      = {2199-0328},
  doi       = {10.25972/OPUS-29335},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-293354},
  pages     = {248},
  year      = {2022},
  abstract  = {Das Verbundprojekt „Plattform f{\"u}r das integrierte Management von Kollaborationen in Wertsch{\"o}pfungsnetzwerken" (PIMKoWe - F{\"o}rderkennzeichen „02P17D160") ist ein Forschungsvorhaben im Rahmen des Forschungsprogramms „Innovationen f{\"u}r die Produktion, Dienstleistung und Arbeit von morgen" der Bekanntmachung „Industrie 4.0 - Intelligente Kollaborationen in dynamischen Wertsch{\"o}pfungs-netzwerken" (InKoWe). Das Forschungsvorhaben wurde mit Mitteln des Bundesministeriums f{\"u}r Bildung und Forschung (BMBF) gef{\"o}rdert und durch den Projekttr{\"a}ger des Karlsruher Instituts f{\"u}r Technologie (PTKA) betreut. Ziel des Forschungsprojekts PIMKoWe ist die Entwicklung und Bereitstellung einer Plattforml{\"o}sung zur Flexibilisierung, Automatisierung und Absicherung von Kooperationen in Wertsch{\"o}pfungsnetzwerken des industriellen Sektors.},
  subject      = {Blockchain},
  language  = {de}
}
@article{OberdorfSchaschekWeinzierletal.2023,
  author    = {Oberdorf, Felix and Schaschek, Myriam and Weinzierl, Sven and Stein, Nikolai and Matzner, Martin and Flath, Christoph M.},
  title     = {Predictive end-to-end enterprise process network monitoring},
  series = {Business \& Information Systems Engineering},
  volume    = {65},
  journal   = {Business \& Information Systems Engineering},
  number    = {1},
  issn      = {2363-7005},
  doi       = {10.1007/s12599-022-00778-4},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-323814},
  pages     = {49-64},
  year      = {2023},
  abstract  = {Ever-growing data availability combined with rapid progress in analytics has laid the foundation for the emergence of business process analytics. Organizations strive to leverage predictive process analytics to obtain insights. However, current implementations are designed to deal with homogeneous data. Consequently, there is limited practical use in an organization with heterogeneous data sources. The paper proposes a method for predictive end-to-end enterprise process network monitoring leveraging multi-headed deep neural networks to overcome this limitation. A case study performed with a medium-sized German manufacturing company highlights the method's utility for organizations.},
  language  = {en}
}