## 5 Conclusions  
Bibliometric analyses are a quantitative approach for literature analysis and help researchers to keep track of their field of research. Furthermore, research managers can use it to quantify academic output. Until now, conducting bibliometric analyses required highly skilled individuals with statistical knowledge and programming skills. Consequently, to make these methods available for a broader audience, the complexity of the application of bibliometric analyses should be reduced. Software systems can help to achieve this goal. However, available tools either require mathematical and programming skills or do not offer comprehensive functionality simultaneously.  
This paper contributes ambalytics, a microservice platform that is capable of calculating research fronts networks (as the first calculation on top of the presented architecture) in a scalable manner and embodies a foundation for a system that meets the demands for a platform capable of feature-rich bibliometric analyses. Based on a software requirement specification, the system's concept was presented. Due to its distributed nature, different microservices and containers are the foundation of the system, which are deployed in a Kubernetes environment.  
Various containers for computing bibliometric networks, such as research fronts networks and the application of community detection algorithms, have been implemented. The execution of analyses containers was orchestrated by using Kubernetes Jobs. Bibliometric data is made available via different data structures in a runtime-optimized way, which enables fast search results on bibliometric subgraphs.  
The system is designed to be scalable and extensible, for example, to support further bibliometric network calculations and bibliometric methods. However, several technical issues are still open and require further research. For example, to support complex multi-step analyses, a scheduler implementing coordinated batch processing, on top of the existing Kubernetes Job API, is needed. Furthermore, investigations are needed to improve the overall performance by adding multi-core or GPU implementations for computation-intensive analyses.  
Additionally, support for further bibliographic data providers may be added, and other data sources may be integrated. Finally, the web-based interface must be continuously evolved, e.g., by a feature creating reports on research trends. We also plan to develop a mobile application that will provide an integrated visualization of publication graphs. More importantly, two studies must be conducted. One to evaluate the usability of the system and one that investigates the performance of the system in terms of scalability.  
As discussed, ambalytics is only the first step toward a powerful tool that fulfills the intended goals of an easy-to-use system; however, we consider the creation of a flexible architecture as an important step. Since we were able to realize the calculation of hybrid research fronts as the first implemented feature on top of the architecture, ambalytics and its overall design appear promising.  
Conceptualization, K.K. and M.G.; data curation, M.G.; writing--original draft preparation, K.K.; writing--review and editing, R.P., M.G. and M.R.; supervision, R.P. and M.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data sharing is not applicable to this article.  
The authors declare no conflict of interest.