Visual Exploration of Financial Data with Incremental Domain Knowledge.

Information Visualization Visual Analytics Visualization Visualization in Finance

Journal

Computer graphics forum : journal of the European Association for Computer Graphics
ISSN: 0167-7055
Titre abrégé: Comput Graph Forum
Pays: England
ID NLM: 101511479

Informations de publication

Date de publication:
Feb 2023
Historique:
medline: 1 2 2023
pubmed: 1 2 2023
entrez: 20 3 2024
Statut: ppublish

Résumé

Modelling the dynamics of a growing financial environment is a complex task that requires domain knowledge, expertise and access to heterogeneous information types. Such information can stem from several sources at different scales, complicating the task of forming a holistic impression of the financial landscape, especially in terms of the economical relationships between firms. Bringing this scattered information into a common context is, therefore, an essential step in the process of obtaining meaningful insights about the state of an economy. In this paper, we present

Identifiants

pubmed: 38504907
doi: 10.1111/cgf.14723
pii: CGF14723
pmc: PMC10946466
doi:

Types de publication

Journal Article

Langues

eng

Pagination

101-116

Informations de copyright

© 2022 The Authors. Computer Graphics Forum published by Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd.

Auteurs

Alessio Arleo (A)

TU Wien Vienna Austria.
Centre for Visual Analytics Science and Technology (CVAST) Vienna Austria.

Christos Tsigkanos (C)

TU Wien Vienna Austria.
Distributed Systems Group (DSG) Vienna Austria.

Roger A Leite (RA)

TU Wien Vienna Austria.
Centre for Visual Analytics Science and Technology (CVAST) Vienna Austria.

Schahram Dustdar (S)

TU Wien Vienna Austria.
Distributed Systems Group (DSG) Vienna Austria.

Silvia Miksch (S)

TU Wien Vienna Austria.
Centre for Visual Analytics Science and Technology (CVAST) Vienna Austria.

Johannes Sorger (J)

Complexity Science Hub Vienna Austria.

Classifications MeSH