@incollection{DiekmannEggert2021,
  author    = {Diekmann, Julian and Eggert, Mathias},
  title     = {Is a Progressive Web App an Alternative for Native App Development?},
  series = {3. Wissenschaftsforum: Digitale Transformation (WiFo21) (Lecture Notes in Informatics ; P-319)},
  booktitle = {3. Wissenschaftsforum: Digitale Transformation (WiFo21) (Lecture Notes in Informatics ; P-319)},
  publisher = {Gesellschaft f{\"u}r Informatik},
  address   = {Darmstadt},
  isbn      = {978-3-88579-713-5},
  pages     = {35 -- 48},
  year      = {2021},
  abstract  = {The existence of several mobile operating systems, such as Android and iOS, is a challenge for developers because the individual platforms are not compatible with each other and require separate app developments. For this reason, cross-platform approaches have become popular but lack in cloning the native behavior of the different operating systems. Out of the plenty cross-platform approaches, the progressive web app (PWA) approach is perceived as promising but needs further investigation. Therefore, the paper at hand aims at investigating whether PWAs are a suitable alternative for native apps by developing a PWA clone of an existing app. Two surveys are conducted in which potential users test and evaluate the PWA prototype with regard to its usability. The survey results indicate that PWAs have great potential, but cannot be treated as a general alternative to native apps. For guiding developers when and how to use PWAs, four design guidelines for the development of PWA-based apps are derived based on the results.},
  language  = {en}
}
@inproceedings{SchulteEggert2021,
  author    = {Schulte, Maximilian and Eggert, Mathias},
  title     = {Predicting hourly bitcoin prices based on long short-term memory neural networks},
  series = {Proceedings of the International Conference on Wirtschaftsinformatik (WI) 2021},
  booktitle = {Proceedings of the International Conference on Wirtschaftsinformatik (WI) 2021},
  pages     = {16 Seiten},
  year      = {2021},
  abstract  = {Bitcoin is a cryptocurrency and is considered a high-risk asset class whose price changes are difficult to predict. Current research focusses on daily price movements with a limited number of predictors. The paper at hand aims at identifying measurable indicators for Bitcoin price movement s and the development of a suitable forecasting model for hourly changes. The paper provides three research contributions. First, a set of significant indicators for predicting the Bitcoin price is identified. Second, the results of a trained Long Short-term Memory (LSTM) neural network that predicts price changes on an hourly basis is presented and compared with other algorithms. Third, the results foster discussions of the applicability of neural nets for stock price predictions. In total, 47 input features for a period of over 10 months could be retrieved to train a neural net that predicts the Bitcoin price movements with an error rate of 3.52 \%.},
  language  = {en}
}