Affiliations: [a] Department of New Media Technology, MODUL University Vienna, Am Kahlenberg 1, 1190 Vienna, Austria. E-mails: adrian.brasoveanu@modul.ac.at, alexander.hubmann@modul.ac.at, daniel.fischl@modul.ac.at | [b] Christian Doppler Laboratory for Software Engineering Integration for Flexible Automation Systems, Vienna University of Technology, Favoritenstrasse 9-11, 1040 Vienna, Austria. E-mail: marta.sabou@ifs.tuwien.ac.at | [c] webLyzard technology gmbh, Puechlgasse 2/44, 1190 Vienna, Austria. E-mail: scharl@weblyzard.com
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
