Sabrina: Modeling and Visualization of Economy Data with Incremental Domain Knowledge
This work addresses the challenge for financial analysts in investment planning by integrating diverse data sources, though it appears incremental as it builds on existing visualization and data fusion methods.
The paper tackles the problem of scattered heterogeneous financial data by presenting Sabrina, an approach that fuses firm-level ground truth with incremental domain knowledge to generate firm-to-firm transaction networks and provides a uniform visual interface, easing the analysis process for financial analysts in a user study with three experts.
Investment planning requires knowledge of the financial landscape on a large scale, both in terms of geo-spatial and industry sector distribution. There is plenty of data available, but it is scattered across heterogeneous sources (newspapers, open data, etc.), which makes it difficult for financial analysts to understand the big picture. In this paper, we present Sabrina, a financial data analysis and visualization approach that incorporates a pipeline for the generation of firm-to-firm financial transaction networks. The pipeline is capable of fusing the ground truth on individual firms in a region with (incremental) domain knowledge on general macroscopic aspects of the economy. Sabrina unites these heterogeneous data sources within a uniform visual interface that enables the visual analysis process. In a user study with three domain experts, we illustrate the usefulness of Sabrina, which eases their analysis process.