@inproceedings{AuthCzarneckiBensberg2019,
  author    = {Auth, Gunnar and Czarnecki, Christian and Bensberg, Frank},
  title     = {Impact of robotic process automation on enterprise architectures},
  series = {GI Edition Proceedings Band 295 INFORMATIK 2019, Workshop-Beitr{\"a}ge},
  booktitle = {GI Edition Proceedings Band 295 INFORMATIK 2019, Workshop-Beitr{\"a}ge},
  editor    = {Draude, Claude and Lange, Martin and Sick, Bernhard and Gesellschaft f{\"u}r Informatik e.V. (GI),},
  publisher = {K{\"o}llen},
  address   = {Bonn},
  isbn      = {9783885796893},
  issn      = {1617-5468},
  doi       = {10.18420/inf2019_ws05},
  pages     = {59 -- 65},
  year      = {2019},
  abstract  = {The initial idea of Robotic Process Automation (RPA) is the automation of business processes through the presentation layer of existing application systems. For this simple emulation of user input and output by software robots, no changes of the systems and architecture is required. However, considering strategic aspects of aligning business and technology on an enterprise level as well as the growing capabilities of RPA driven by artificial intelligence, interrelations between RPA and Enterprise Architecture (EA) become visible and pose new questions. In this paper we discuss the relationship between RPA and EA in terms of perspectives and implications. As workin- progress we focus on identifying new questions and research opportunities related to RPA and EA.},
  language  = {en}
}
@inproceedings{BensbergAuthCzarneckietal.2018,
  author    = {Bensberg, Frank and Auth, Gunnar and Czarnecki, Christian and W{\"o}rndle, Christopher},
  title     = {Transforming literature-intensive research processes through text analytics - design, implementation and lessons learned},
  editor    = {Kemal İlter, H.},
  doi       = {10.6084/m9.figshare.7582073.v1},
  pages     = {9 Seiten},
  year      = {2018},
  abstract  = {The continuing growth of scientific publications raises the question how research processes can be digitalized and thus realized more productively. Especially in information technology fields, research practice is characterized by a rapidly growing volume of publications. For the search process various information systems exist. However, the analysis of the published content is still a highly manual task. Therefore, we propose a text analytics system that allows a fully digitalized analysis of literature sources. We have realized a prototype by using EBSCO Discovery Service in combination with IBM Watson Explorer and demonstrated the results in real-life research projects. Potential addressees are research institutions, consulting firms, and decision-makers in politics and business practice.},
  language  = {en}
}
@inproceedings{HoevelerJanser2016,
  author    = {Hoeveler, Bastian and Janser, Frank},
  title     = {The aerodynamically optimized design of a fan-in-wing duct},
  series = {Applied Aerodynamics Research Conference 2016, Bristol, GB, Jul 19-21, 2016},
  booktitle = {Applied Aerodynamics Research Conference 2016, Bristol, GB, Jul 19-21, 2016},
  isbn      = {1-85768-371-4},
  pages     = {1 -- 10},
  year      = {2016},
  language  = {en}
}
@inproceedings{DinghoferHartung2020,
  author    = {Dinghofer, Kai and Hartung, Frank},
  title     = {Analysis of Criteria for the Selection of Machine Learning Frameworks},
  series = {2020 International Conference on Computing, Networking and Communications (ICNC)},
  booktitle = {2020 International Conference on Computing, Networking and Communications (ICNC)},
  doi       = {10.1109/ICNC47757.2020.9049650},
  pages     = {373 -- 377},
  year      = {2020},
  language  = {en}
}
@inproceedings{GaldiHartungDugelay2017,
  author    = {Galdi, Chiara and Hartung, Frank and Dugelay, Jean-Luc},
  title     = {Videos versus still images: Asymmetric sensor pattern noise comparison on mobile phones},
  series = {Electronic Imaging},
  booktitle = {Electronic Imaging},
  publisher = {Society for Imaging Science and Technology},
  address   = {Springfield, Virginia},
  issn      = {2470-1173},
  doi       = {10.2352/ISSN.2470-1173.2017.7.MWSF-331},
  pages     = {100 -- 103},
  year      = {2017},
  abstract  = {Nowadays, the most employed devices for recoding videos or capturing images are undoubtedly the smartphones. Our work investigates the application of source camera identification on mobile phones. We present a dataset entirely collected by mobile phones. The dataset contains both still images and videos collected by 67 different smartphones. Part of the images consists in photos of uniform backgrounds, especially collected for the computation of the RSPN. Identifying the source camera given a video is particularly challenging due to the strong video compression. The experiments reported in this paper, show the large variation in performance when testing an highly accurate technique on still images and videos.},
  language  = {en}
}