Exploration project AI in nursing: data, prerequisites, framework

Principal investigator: Prof. Dr. Daniel Fürstenau (FUB, ECDF)

Project participants: Matthias Schulte-Althoff (FUB), Prof. Dr. Karin Wolf-Ostermann (Uni-Bremen), Dominik Domhoff (Uni-Bremen), Kathrin Seibert (Uni-Bremen), Sarah Theune (Vediso), Prof. Dr. Felix Biessemann (Beuth Hochschule), Anastasia Blank (FUB). 

Project description: Artificial intelligence is regarded as a crucial technology for solving many of today’s challenges in the field of care. We aim to identify the prerequisites for successful research in this field and to develop a framework for future research projects on artificial intelligence in care settings. Together with the Department of Nursing Science Care Research of the Institute for Public Health and Nursing Research (IPP) at the University of Bremen, the Beuth Hochschule and the Association for Digitalization in the Social Economy e.V. (Vediso), funded by the German Federal Ministry of Education and Research (BMBF), our exploratory project on AI in Nursing (SoKIP) aims at a participatory approach by collecting findings and promoting interdisciplinary cooperation in the implementation of AI systems in nursing. Our research will focus on software-based AI systems outside of robotics and will investigate best practices in the development of targeted AI solutions in nursing. It is important to explore those areas that are most relevant to the needs of caregivers, while taking into account the opinions and needs of a wide range of stakeholders in the care sector, including those in need of care.

Sponsored by: Bundesminesterium für Bildung und Forschung (BMBF)

DIPA-SCALE: Digital Platforms – Mechanisms of Establishment and Scaling

Principal investigator: Prof. Dr. Daniel Fürstenau (FUB, ECDF)

Project participants: Matthias Schulte-Althoff (FUB), Kai Schewina (FUB), Kristina Meindl (FUB), Prof. Claudia Spies (Charité, ECDF), Prof. Hannes Rothe (FUB, Digital Entrepreneurship Hub), Prof. Abayomi Baiyere (Copenhagen Business School)

Project description: The digitalization leads to the transformation of multiple branches. Commerce companies as transform themselves to E-Commerce-Platforms. Traditional banking companies could soon transform to FinTech-Platforms. In the health industry the traditional model of episodic care processes in some institutions could be enriched or replaced by integrated care processes. In all these cases internal digital infrastructures in companies are complemented by externally opened digital platforms focussed on innovation. In this project the focus lies on digital platforms, including the partner-ecosystems. This is considered from the perspective of socio-technological networks, whereby respective visualizations and analyses are displayed on multiple levels (e.g. connections within the IT-architecture with components/services/systems, connections within the partner ecosystem). In doing so a network-analytical framework is applied. Temporal dynamics are being examined explicitly. A data-/model-driven approach is pursued, existing approaches are being considered and further developed. The examination of mechanisms of the establishment and scaling of digital platforms is focussed.

Sponsored by: Einstein Center Digital Future

Tech Stack Analysis: Drivers and Risk-related Outcomes in the Process of Digital Infrastructure Adoption

Principal investigator: Matthias Schulte-Althoff (FUB)

Project participants: Prof. Dr. Daniel Fürstenau (FUB, ECDF)

Project start: 04/2018

Project description: We wish to suggest a dynamic model of digital infrastructure of startups in ecosystems. This model builds on technology stacks as found in public data aggregators. In the first part of the thesis, we examine which factors drive the digitial infrastructure of startups to become more homogeneous or heterogeneous over time. Using technology stack adoption as dependent variable and applying advanced time series analysis and network evolution modeling, we investigate to what extent similar or different technology stacks occur and how this is driven by the ecosystem in which a startup is embedded. In the second part of the thesis, we shed light on technology stack-related risks in start-up ecosystems that combine vulnerability, technology stack, and start-up data by providing a quantification of the criticality of technology stacks.

Urban planning
TMBPS – Text Mining of Social Media Data as a Form of Citizen Participation in Urban Planning Processes

Principal investigator: Dr. Daniel Fürstenau (Projektleiter FUB, ECDF)

Project participants: Matthias Schulte-Althoff (FUB), Flavio Morelli (FUB), Prof. Jochen Rabe (TUB, ECDF)

Project start: 09/2018

Project description: Citizen participation enables the integration of citizens‘ opinions into the urban planning process. This makes it possible to derive urban planning measures that are more effective and more acceptable to society. In recent years, various approaches have been developed that have simplified the participation of citizens with new technologies. One method that has hardly been researched in this context is Topic Modeling. Topic modeling can be used to discover the topics contained in a corpus. This project uses Twitter data to investigate the extent to which Topic Modeling can be used for the analysis and visualization of citizens‘ views and the limitations of these methods. An indicator is proposed to analyze the temporal development of topics in a district. In this way, the change in citizens‘ priorities can be better understood in an interdisciplinary manner and in the context of the Einstein Center Digital Future.

CCA – CryptoCurrencyAnalysis: The influence of Tweets on the price of cryptocurrencies

Principal investigator: Prof. Dr. Daniel Fürstenau (Projektleiter FUB, ECDF)

Project participants: Matthias Schulte-Althoff (FUB), Thuy Duong Le (FUB), Kai Schewina (FUB), Prof. Christian Meske (FUB, ECDF), Prof. Peter Mohr (FUB)

Project start: 11/2018

Project description: High return opportunities are increasingly bringing cryptocurrencies into the focus of public attention. Above all Bitcoin and the Blockchain are widely studied. When considering determinants for the price formation of cryptocurrencies, emotional factors have often been neglected. Therefore, the project investigates the relationship between emotions and the price of cryptocurrencies such as Bitcoin and others. The main goal is to check the discovered relationships for theoretical conformity. Therefore, sentiment data is derived from Twitter messages with a reference to different cryptocurrencies. The data analysis is carried out with the help of statistical methods such as vector autoregressive models. In addition, the Granger causality test should allow a broader perspective on the relationship between the different factors In addition, we evaluate the results for robustness and prognostic power. The work has an interdisciplinary character and takes place in the context of the Einstein Center Digital Future.


Principal investigator: Prof. Dr. Daniel Fürstenau (Projektleiter FUB, ECDF)

Project participants: Matthias Schulte-Althoff (FUB), Gabriella Volpe (FUB)

Project start: 10/2018

Project description: Together with students of business informatics, the humanoid robot Pepper is being developed. In a university environment, speech capabilities, interaction with humans and emotional abilities realized by artificial intelligence are being tried out. Use cases in banking are tested in the „Bird’s Nest“, the innovation lab of the Berliner Sparkasse.