@inproceedings{4254,
  abstract     = {{The current trend of integrating machines and factories into cyber-physical systems (CPS) creates an enormous complexity for operators of such systems. Especially the search for the root cause of cascading failures becomes highly time-consuming. Within this paper, we address the question on how to help human users to better and faster understand root causes of such situations. We propose a concept of interactive alarm flood reduction and present the implementation of a first vertical prototype for such a system. We consider this prototype as a first artifact to be discussed by the research community and aim towards an incremental further development of the system in order to support humans in complex error situations.}},
  author       = {{Büttner, Sebastian and Wunderlich, Paul and Heinz, Mario and Niggemann, Oliver and Röcker, Carsten}},
  booktitle    = {{ Machine Learning and Knowledge Extraction : First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio, Italy, August 29 – September 1, 2017, Proceedings}},
  editor       = {{Holzinger, Andreas}},
  isbn         = {{978-3-319-66807-9}},
  keywords     = {{Alarm flood reduction, Machine learning, Assistive system}},
  location     = {{Reggio, Italy}},
  pages        = {{69--82}},
  publisher    = {{Springer}},
  title        = {{{Managing Complexity: Towards Intelligent Error-Handling Assistance Trough Interactive Alarm Flood Reduction}}},
  volume       = {{10410}},
  year         = {{2017}},
}

@inproceedings{4255,
  abstract     = {{Increasingly, production processes are enabled and controlled by Information Technology (IT), a development being also referred to as “Industry 4.0”. IT thereby contributes to flexible and adaptive production processes, and in this sense factories become “smart factories”. In line with this, IT also more and more supports human workers via various assistance systems. This support aims to both support workers to better execute their tasks and to reduce the effort and time required when working. However, due to the large spectrum of assistance systems, it is hard to acquire an overview and to select an adequate system for a smart factory based on meaningful criteria. We therefore synthesize a set of comparison criteria into a consistent framework and demonstrate the application of our framework by classifying three examples.}},
  author       = {{Fellmann, Michael and Robert, Sebastian and Büttner, Sebastian and Mucha, Henrik and Röcker, Carsten}},
  booktitle    = {{ Machine Learning and Knowledge Extraction : First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio, Italy, August 29 – September 1, 2017, Proceedings}},
  editor       = {{Holzinger, Andreas}},
  isbn         = {{978-3-319-66807-9}},
  keywords     = {{Assistance systems, Smart factory, Production processes}},
  location     = {{Reggio, Italy}},
  pages        = {{59--68}},
  publisher    = {{Springer}},
  title        = {{{Towards a Framework for Assistance Systems to Support Work Processes in Smart Factories}}},
  doi          = {{10.1007/978-3-319-66808-6_5}},
  volume       = {{10410}},
  year         = {{2017}},
}