@inproceedings{KehrerJugelZimmermann2016,
  author    = {Kehrer, Stefan and Jugel, Dierk and Zimmermann, Alfred},
  title     = {A systematic literature review of big data literature for EA evolution},
  booktitle = {Digital Enterprise Computing (DEC 2016) : B{\"o}blingen, Germany, June 14 - 15, 2016},
  editor    = {Hertweck, Dieter},
  isbn      = {978-3-88579-652-7},
  url       = {http://subs.emis.de/LNI/Proceedings/Proceedings258/article10.html},
  institution = {Informatik},
  pages     = {209 -- 220},
  year      = {2016},
  abstract  = {Many organizations identified the opportunities of big data analytics to support the business with problem-specific insights through the exploitation of generated data. Socio-technical solutions are developed in big data projects to reach competitive advantage. Although these projects are aligned to specific business needs, common architectural challenges are not addressed in a comprehensive manner. Enterprise architecture management is a holistic approach to tackle the complex business and IT architecture. The transformation of an organization's EA is influenced by big data projects and their data-driven approach on all layers. To enable strategy­ oriented development of the EA it is essential to synchronize these projects supported by EA management. In this paper, we conduct a systematic review of big data literature to analyze which requirements for the EA management discipline are proposed. Thereby, a broad overview about existing research is presented to facilitate a more detailed exploration and to foster the evolution o the EA management discipline.},
  language  = {en}
}