Visualizing statistical linked knowledge for decision support

Issue title: Visual exploration and analysis of Linked Data

Guest editors: Aba-Sah Dadzie and Emmanuel Pietriga

Article type: Research Article

Authors: Braşoveanu, Adrian M.P.^{a; c; *} | Sabou, Marta^b | Scharl, Arno^{a; c} | Hubmann-Haidvogel, Alexander^{a; c} | Fischl, Daniel^a

Affiliations: [a] Department of New Media Technology, MODUL University Vienna, Am Kahlenberg 1, 1190 Vienna, Austria. E-mails: [email protected], [email protected], [email protected] | [b] Christian Doppler Laboratory for Software Engineering Integration for Flexible Automation Systems, Vienna University of Technology, Favoritenstrasse 9-11, 1040 Vienna, Austria. E-mail: [email protected] | [c] webLyzard technology gmbh, Puechlgasse 2/44, 1190 Vienna, Austria. E-mail: [email protected]

Correspondence: [*] Corresponding author. E-mail: [email protected].

Abstract: In a global and interconnected economy, decision makers often need to consider information from various domains. A tourism destination manager, for example, has to correlate tourist behavior with financial and environmental indicators to allocate funds for strategic long-term investments. Statistical data underpins a broad range of such cross-domain decision tasks. A variety of statistical datasets are available as Linked Open Data, often incorporated into visual analytics solutions to support decision making. What are the principles, architectures, workflows and implementation design patterns that should be followed for building such visual cross-domain decision support systems. This article introduces a methodology to integrate and visualize cross-domain statistical data sources by applying selected RDF Data Cube (QB) principles. A visual dashboard built according to this methodology is presented and evaluated in the context of two use cases in the tourism and telecommunications domains.

Keywords: Linked Data, information visualization, Decision Support Systems, RDF Data Cube, data analytics

DOI: 10.3233/SW-160225

Journal: Semantic Web, vol. 8, no. 1, pp. 113-137, 2017