Results & Achievements
A living record of the technological milestones, research findings and measurable outcomes delivered by the Moneying-Plus platform.
Key Results
Concrete outcomes produced throughout the project lifecycle, from algorithm benchmarking to live platform integrations.
Research on database technologies for large dataset analysis
Research and technology evaluation activity focused on identifying database solutions suitable for managing and analyzing massive amounts of data. During the study, JanusGraph was identified as a technology of particular interest for scenarios based on complex relationships and advanced data analysis. In parallel, the potential of BerkeleyDB was also analyzed, evaluating its features, performance, and possible application areas within architectures oriented toward scalability and large-scale dataset processing.
Research on algorithms for connected nodes analysis and performance comparison
Research and experimentation activity aimed at analyzing known algorithms for identifying and studying strongly connected components within graph data structures. During the analysis, Kosaraju's and Tarjan's algorithms were identified as reference solutions for the examined application context. Both were implemented within a prototype developed using ad-hoc synthetic datasets, with the objective of performing performance comparisons, evaluating computational efficiency, and analyzing behavior in simulated scenarios of increasing complexity.
Milestones
The key moments that shaped the development of the Moneying-Plus platform.
Launch and development of research activities on database technologies oriented towards large-scale data analysis. During this milestone, specialized solutions for high relational complexity scenarios were evaluated, with a focus on JanusGraph, alongside an in-depth study of BerkeleyDB's potential regarding performance, scalability, and possible adoption areas.
Commencement of research and prototyping activities dedicated to the analysis of algorithms for identifying strongly connected components in graph structures. Kosaraju's and Tarjan's algorithms were selected and implemented within a prototype based on ad-hoc synthetic datasets to perform performance comparisons and evaluate their effectiveness in simulated scenarios.
