@article{BrockmannGronauSultanow2008,
  author    = {Brockmann, Carsten and Gronau, Norbert and Sultanow, Eldar},
  title     = {ERP und MES : Teil 3},
  issn      = {1617-948X},
  year      = {2008},
  language  = {de}
}
@article{KokhanovskyLamareDanneetal.2019,
  author    = {Kokhanovsky, Alexander and Lamare, Maxim and Danne, Olaf and Brockmann, Carsten and Dumont, Marie and Picard, Ghislain and Arnaud, Laurent and Favier, Vincent and Jourdain, Bruno and Le Meur, Emmanuel and Di Mauro, Biagio and Aoki, Teruo and Niwano, Masashi and Rozanov, Vladimir and Korkin, Sergey and Kipfstuhl, Sepp and Freitag, Johannes and Hoerhold, Maria and Zuhr, Alexandra and Vladimirova, Diana and Faber, Anne-Katrine and Steen-Larsen, Hans Christian and Wahl, Sonja and Andersen, Jonas K. and Vandecrux, Baptiste and van As, Dirk and Mankoff, Kenneth D. and Kern, Michael and Zege, Eleonora and Box, Jason E.},
  title     = {Retrieval of Snow Properties from the Sentinel-3 Ocean and Land Colour Instrument},
  series = {Remote sensing},
  volume    = {11},
  journal   = {Remote sensing},
  number    = {19},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2072-4292},
  doi       = {10.3390/rs11192280},
  pages     = {43},
  year      = {2019},
  abstract  = {The Sentinel Application Platform (SNAP) architecture facilitates Earth Observation data processing. In this work, we present results from a new Snow Processor for SNAP. We also describe physical principles behind the developed snow property retrieval technique based on the analysis of Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A/B measurements over clean and polluted snow fields. Using OLCI spectral reflectance measurements in the range 400-1020 nm, we derived important snow properties such as spectral and broadband albedo, snow specific surface area, snow extent and grain size on a spatial grid of 300 m. The algorithm also incorporated cloud screening and atmospheric correction procedures over snow surfaces. We present validation results using ground measurements from Antarctica, the Greenland ice sheet and the French Alps. We find the spectral albedo retrieved with accuracy of better than 3\% on average, making our retrievals sufficient for a variety of applications. Broadband albedo is retrieved with the average accuracy of about 5\% over snow. Therefore, the uncertainties of satellite retrievals are close to experimental errors of ground measurements. The retrieved surface grain size shows good agreement with ground observations. Snow specific surface area observations are also consistent with our OLCI retrievals. We present snow albedo and grain size mapping over the inland ice sheet of Greenland for areas including dry snow, melted/melting snow and impurity rich bare ice. The algorithm can be applied to OLCI Sentinel-3 measurements providing an opportunity for creation of long-term snow property records essential for climate monitoring and data assimilation studies-especially in the Arctic region, where we face rapid environmental changes including reduction of snow/ice extent and, therefore, planetary albedo.},
  language  = {en}
}
@inproceedings{VladovaUllrichSultanowetal.2023,
  author    = {Vladova, Gergana and Ullrich, Andr{\´e} and Sultanow, Eldar and Tobolla, Marinho and Sebrak, Sebastian and Czarnecki, Christian and Brockmann, Carsten},
  title     = {Visual analytics for knowledge management},
  series = {Informatik 2023},
  booktitle = {Informatik 2023},
  editor    = {Klein, Maike and Krupka, Daniel and Winter, Cornelia and Wohlgemuth, Volker},
  publisher = {Gesellschaft f{\"u}r Informatik e.V. (GI)},
  address   = {Bonn},
  isbn      = {978-3-88579-731-9},
  issn      = {1617-5468},
  doi       = {10.18420/inf2023_187},
  pages     = {1851 -- 1870},
  year      = {2023},
  abstract  = {The management of knowledge in organizations considers both established long-term processes and cooperation in agile project teams. Since knowledge can be both tacit and explicit, its transfer from the individual to the organizational knowledge base poses a challenge in organizations. This challenge increases when the fluctuation of knowledge carriers is exceptionally high. Especially in large projects in which external consultants are involved, there is a risk that critical, company-relevant knowledge generated in the project will leave the company with the external knowledge carrier and thus be lost. In this paper, we show the advantages of an early warning system for knowledge management to avoid this loss. In particular, the potential of visual analytics in the context of knowledge management systems is presented and discussed. We present a project for the development of a business-critical software system and discuss the first implementations and results.},
  language  = {en}
}