A key factor of software architecting is the decision-making process. All phases of software development contain some kind of decision-making activities. However, the software architecture decision process is the most challenging part. To support the decision-making process, a research project named ORION provided a knowledge repository that contains a collection of decision cases. To utilize the collected data in an efficient way, eliciting correlations between decision cases needs to be automated.  The objective of this thesis is to select appropriate method(s) for automatically detecting correlations between decision cases. To do this, an experiment was conducted using a dataset of collected decision cases that are based on a taxonomy called GRADE. The dataset is stored in the Neo4j graph database. The Neo4j platform provides a library of graph algorithms which allow to analyse a number of relationships between connected data. In this experiment, five Similarity algorithms are used to find correlated decisions, then the algorithms are analysed to determine whether the they would help improve decision-making.  From the results, it was concluded that three of the algorithms can be used as a source of support for decision-making processes, while the other two need further analyses to determine if they provide any support.

In recent years, the pervasive issue of bullying, particularly in academic institutions, has witnessed a surge in attention. This report centers around the utilization of the Friends Dataset and Graph Machine Learning to detect possible instances of bullying in an educational setting. The importance of this research lies in the potential it has to enhance early detection and prevention mechanisms, thereby creating safer environments for students. Leveraging graph theory, Neo4j, Graph Data Science Library, and similarity algorithms, among other tools and methods, we devised an approach for processing and analyzing the dataset. Our method involves data preprocessing, application of similarity and community detection algorithms, and result validation with domain experts. The findings of our research indicate that Graph Machine Learning can be effectively utilized to identify potential bullying scenarios, with a particular focus on discerning community structures and their influence on bullying. Our results, albeit preliminary, represent a promising step towards leveraging technology for bullying detection and prevention.