@inproceedings{SchmidtZimmermannKelleretal.2020,
  author    = {Schmidt, Rainer and Zimmermann, Alfred and Keller, Barbara and M{\"o}hring, Michael},
  title     = {Towards engineering artificial intelligence-based applications},
  booktitle = {2020 IEEE 24th International Enterprise Distributed Object Computing Workshop (EDOCW), proceedings, Eindhoven, Netherlands, 5 October 2020},
  isbn      = {978-1-7281-6471-7},
  issn      = {2325-6605},
  doi       = {10.1109/EDOCW49879.2020.00020},
  institution = {Informatik},
  pages     = {54 -- 62},
  year      = {2020},
  abstract  = {AI technologies such as deep learning provide promising advances in many areas. Using these technologies, enterprises and organizations implement new business models and capabilities. In the beginning, AI-technologies have been deployed in an experimental environment. AI-based applications have been created in an ad-hoc manner and without methodological guidance or engineering approach. Due to the increasing importance of AI-technologies, however, a more structured approach is necessary that enable the methodological engineering of AI-based applications. Therefore, we develop in this paper first steps towards methodological engineering of AI-based applications. First, we identify some important differences between the technological foundations of AI- technologies, in particular deep learning, and traditional information technologies. Then we create a framework that enables to engineer AI-applications using four steps: identification of an AI-application type, sub-type identification, lifecycle phase, and definition of details. The introduced framework considers that AI-applications use an inductive approach to infer knowledge from huge collections and streams of data. It not only enables the rapid development of AI-application but also the efficient sharing of knowledge on AI-applications.},
  language  = {en}
}