Sabrina: Modeling and Visualization of Economy Data with Incremental Domain Knowledge

Alessio Arleo, Christos Tsigkanos, Chao Jia, Roger Leite, Ilir Murturi, Manfred Klaffenböck, Schahram Dustdar, Silvia Miksch, Michael Wimmer, Johannes Sorger
Sabrina: Modeling and Visualization of Economy Data with Incremental Domain Knowledge
In IEEE VIS 2019. October 2019.

Information

  • Publication Type: Conference Paper
  • Workgroup(s)/Project(s):
  • Date: October 2019
  • Booktitle: IEEE VIS 2019
  • Call for Papers: Call for Paper
  • Event: IEEE Visualization Conference (VIS)
  • Lecturer: Alessio Arleo
  • Location: Vancouver, British Columbia, Canada
  • Open Access: yes
  • Keywords: Visualization, Visual Analytics

Abstract

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.

Additional Files and Images

Additional images and videos

sab_tease_nolabels: The different visualizations composing the Sabrina system sab_tease_nolabels: The different visualizations composing the Sabrina system

Additional files

Weblinks

BibTeX

@inproceedings{Arleo-2019-vis,
  title =      "Sabrina: Modeling and Visualization of Economy Data with
               Incremental Domain Knowledge",
  author =     "Alessio Arleo and Christos Tsigkanos and Chao Jia and Roger
               Leite and Ilir Murturi and Manfred Klaffenb\"{o}ck and
               Schahram Dustdar and Silvia Miksch and Michael Wimmer and
               Johannes Sorger",
  year =       "2019",
  abstract =   "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.",
  month =      oct,
  booktitle =  "IEEE VIS 2019",
  event =      " IEEE Visualization Conference (VIS)",
  location =   "Vancouver, British Columbia, Canada",
  keywords =   "Visualization, Visual Analytics",
  URL =        "/research/publications/2019/Arleo-2019-vis/",
}