@inproceedings{SchmidtM{\"o}hringH{\"a}rtingetal.2015,
  author    = {Schmidt, Rainer and M{\"o}hring, Michael and H{\"a}rting, Ralf-Christian and Zimmermann, Alfred and Heitmann, Jan and Blum, Franziska},
  title     = {Leveraging textual information for improving decision making in the business process lifecycle},
  booktitle = {Intelligent Decision Technologies : Proceedings of the 7th KES International Conference on Intelligent Decision Technologies (KES-IDT 2015)},
  editor    = {Neves-Silva, Rui},
  isbn      = {978-331-91985-7-6},
  doi       = {10.1007/978-3-319-19857-6_48},
  institution = {Informatik},
  pages     = {563 -- 574},
  year      = {2015},
  abstract  = {Business process implementations fail, because requirements are elicited incompletely. At the same time, a huge amount of unstructured data is not used for decision-making during the business process lifecycle. Data from questionnaires and interviews is collected but not exploited because the effort doing so is too high. Therefore, this paper shows how to leverage textual information for improving decision making in the business process lifecycle. To do so, text mining is used for analyzing questionnaires and interviews.},
  language  = {en}
}