Data Transformation Tool to Explore News Recommenders

Author: Manuel Feuerstein

Supervisor: Julia Neidhardt, Co-Supervisor: Thomas E. Kolb


This thesis focuses on the development of a data transformation tool to analyze and leverage diverse data sources from Falter Verlagsgesellschaft m.b.H (Falter). Falter provides content-based data from platforms like and, along with user-based data from Matomo Analytics and shop purchase statistics. The objective is to gain deeper insights into user behavior on their websites and the content they engage with. The central challenge is to build a versatile system capable of transforming, visualizing, and analyzing this data while ensuring adaptability for future data integration. The motivation behind this project is twofold: to harness contemporary technology for data analysis and to establish a reusable system for forthcoming data initiatives. The initial data resides in an MS-SQL database and requires assessment to determine the necessary transformations for research purposes. The choice of technology for this endeavor remains flexible, guided by a comprehensive literature review. Given the software engineering orientation of this thesis, emphasis is placed on technology analysis and implementation to ensure the final product’s reusability, thus avoiding redundant efforts. The anticipated outcomes include the analysis, transformation, and visualization of Falter’s data to facilitate future data evaluations. The resulting artifact should be able to achieve the following tasks data transformation, visualize interrelationships among data sets, and exhibit a high degree of reusability for similar data sets, fostering efficient data exploration and analysis.