TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Schmidt, Rainer
A1  - Zimmermann, Alfred
A1  - Keller, Barbara
A1  - Möhring, Michael
T1  - Towards engineering artificial intelligence-based applications
T2  - 2020 IEEE 24th International Enterprise Distributed Object Computing Workshop (EDOCW), proceedings, Eindhoven, Netherlands, 5 October 2020
N2  - 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.
KW  - artificial intelligence
KW  - application development
KW  - framework
KW  - design science
Y1  - 2020
SN  - 2325-6605
SS  - 2325-6605
SN  - 978-1-7281-6471-7
SB  - 978-1-7281-6471-7
U6  - https://doi.org/10.1109/EDOCW49879.2020.00020
DO  - https://doi.org/10.1109/EDOCW49879.2020.00020
SP  - 54
EP  - 62
S1  - 9
PB  - IEEE
CY  - Piscataway, NJ
ER  -