Abstract This thesis explores the development and implementation of a hybrid Data Management System tailored to the data landscape of SenseLab's experimental framework. By blending relational and graph-based databases, the system aims to manage the diverse datasets generated by SenseLab's experiments effectively. The research begins by analyzing SenseLab's data, followed by selecting criteria for the intended Database Management system, including data integration, management and future-proofness. This leads to the adoption of a hybrid Database Management System architecture. Developing the Database Management System consists of different design stages. After implementing a basic structure, the research continues by testing the developed system with different case studies and query scenarios to evaluate the system's functionality. This includes cross-domain analyses and behavioral data exploration. Following this stage, the benefits and limitations of the chosen architecture are made clear. Implementing the hybrid Database Management System faces software dependencies and multi-user access limitations. The hybrid Database Management System offers a robust framework for managing the diverse dataset provided by SenseLab. By integrating relational and non-relational databases, the system demonstrates its capability to handle various data types and facilitate cross-domain analyses.     «

Abstract This thesis explores the development and implementation of a hybrid Data Management System tailored to the data landscape of SenseLab's experimental framework. By blending relational and graph-based databases, the system aims to manage the diverse datasets generated by SenseLab's experiments effectively. The research begins by analyzing SenseLab's data, followed by selecting criteria for the intended Database Management system, including data integration, management and future-proofnes...     »