Logo des Repositoriums

Auflistung nach Schlagwort "data science"

1 - 4 von 4
  • Textdokument
    AI-based Online Vacancies - Trends and Differences between SMEs and Larger Enterprises
    (INFORMATIK 2022, 2022) Lowin,Maximilian; Seip,Jendrik
    Artificial intelligence is an essential key competence for cost reduction in companies. For this reason, the German government is promoting its use and hopes that it will lead to a digital transformation of companies. We investigated whether small and medium-sized enterprises (SMEs) also participate in this transformation by examining AI-related vacancies for their fields of activity. In particular, we observe whether the required skills of potential applicants suggest that enterprises also require artificial intelligence at an advanced stage and that quality factors such as fairness and ethics play a role. We found that the need for more advanced AI-related jobs has started increasing but still plays a minor role compared to traditional data science roles. Notably, only large companies require some of these additional skills. Moreover, many vacancies even state that a deep AI understanding is not necessarily a prerequisite.
  • Workshopbeitrag
    First International Workshop on Co-Creation of Hybrid Interactive Systems for Healthcare
    (Mensch und Computer 2023 - Workshopband, 2023) Huldtgren, Alina; Klapperich, Holger; Weiler, Tim; Struzek, David; Malmborg, Lone; Rouncefield, Mark; Fischer, Gerhard; Müller, Claudia
    The value of hybrid approaches in healthcare has become apparent, in particular, during the recent Covid-19 pandemic, but remains important post-covid, as hybrid modes of operation can mitigate other issues, e.g. remote healthcare delivery, or sustainable healthcare. The advancement of data science and artificial intelligence enables these hybrid modes of healthcare, but also calls for integrated co-creative design approaches that bring together experts in AI, Socio-Informatics, UX and Ethics as well as citizens and practitioners. Despite a long-standing tradition of participatory approaches within HCI, an analysis of the literature shows that a deeper analysis of the practice of inter- and transdisciplinary participatory research in the healthcare field is missing. Furthermore, the aforementioned technological advancements bring new social, technical and ethical issues to the fore, among others questions of data bias, and empowerment of stakeholders. In this workshop we invite researchers and practitioners from diverse backgrounds to share their experiences and (design) case studies in co-creation of hybrid health systems and learn from contextualized best practices and failures. Through building on these experiences and cases and taking inspiration from praxeological research, we would like to collaborate towards a systematic approach for reflection in co-creation of hybrid healthcare systems.
  • Konferenzbeitrag
    git2net: Mining Time-Stamped Co-Editing Networks from Large git Repositories
    (INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft, 2019) Gote, Christoph; Scholtes, Ingo; Schweitzer, Frank
  • Konferenzbeitrag
    Student Success Prediction and the Trade-Off between Big Data and Data Minimization
    (DeLFI 2018 - Die 16. E-Learning Fachtagung Informatik, 2018) Heuer, Hendrik; Breiter, Andreas
    This paper explores student’s daily activity in a virtual learning environment in the anonymized Open University Learning Analytics Dataset (OULAD). We show that the daily activity of students can be used to predict their success, i.e. whether they pass or fail a course, with high accuracy. This is important since daily activity can be easily obtained and anonymized. To support this, we show that the binary information whether a student was active on a given day has similar predictive power as a combination of the exact number of clicks on the given day and sensitive private data like gender, disability, and highest educational level. We further show that the anonymized activity data can be used to group students. We identify different student types based on their daily binarized activity and outline how educators and system developers can utilize this to address different learning types. Our primary stakeholders are designers and developers of learning analytics systems as well as those who commission such systems. We discuss the privacy and design implications of our findings for data mining in educational contexts against the background of the principle of data minimization and the General Data Protection Regulation (GDPR) of the European Union.