MA Abschlussvortrag, Manuel Göster, Ort: Cisco WebEx, Datum: 20.05.2020, Zeit: 11:00 Uhr

Deep understanding about a field of research is valuable for academic researchers. Additional to technical knowledge, this includes knowledge of subareas, open research questions and social communities (networks) of individuals and organizations within the field. With bibliometric analyses, researchers can acquire quantitatively sound knowledge of a research area by using bibliographic information on academic publications provided by bibliographic data providers. Bibliometric analyses include the calculation of bibliometric networks to describe affiliations or similarities of bibliometric entities, for example authors, and group the latter into clusters representing subareas or communities. Computing and visualizing bibliometric networks is a nontrivial data science task that requires highly skilled individuals and is time-consuming. Besides domain knowledge, researchers must often provide statistics- and programming skills or use software tools having limited functionality and usability.

In this thesis Citarics was developed, a software system that reduces the complexity of bibliometric network analysis and visualization. It accompanies users through the process of bibliometric analysis and eliminates the need for individuals to have programming skills and statistics knowledge while preserving advanced functionality such as algorithm parameterization for experts. As a proof-of-concept, the calculation of research fronts networks is implemented. Being designed to scale, Citarics makes use of distributed systems concepts and technologies. It is based on the microservice architecture concept and uses the Kubernetes framework for orchestration. Citarics depicts the initial building block of a comprehensive bibliometric analysis platform with great usability and scalability.