Quotation Bra ̧soveanu, Adrian M.P., Sabou, Reka Marta, Hubmann-Haidvogel, Alexander, Scharl, Arno, Fischl, Daniel. 2017. Visualizing statistical linked knowledge for decision support. Semantic Web. 8 (1), 113-137.


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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.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Semantic Web
Citation Index SCI
WU-Journal-Rating new INF-A
Language English
Title Visualizing statistical linked knowledge for decision support
Volume 8
Number 1
Year 2017
Page from 113
Page to 137
Reviewed? Y
URL https://content.iospress.com/articles/semantic-web/sw225
DOI http://dx.doi.org/10.3233/SW-160225
Open Access N

Associations

People
Sabou, Reka Marta (Details)
External
Bra ̧soveanu, Adrian M.P. (MODUL University Vienna, Austria)
Fischl, Daniel (MODUL University Vienna, Austria)
Hubmann-Haidvogel, Alexander (MODUL University Vienna, Austria)
Scharl, Arno (MODUL University Vienna, Austria)
Organization
Institute for Data, Process and Knowledge Management (AE Sabou) (Details)
Research areas (ÖSTAT Classification 'Statistik Austria')
1109 Information and data processing (Details)
1122 Artificial intelligence (Details)
1127 Information science (Details)
1138 Information systems (Details)
1140 Software engineering (Details)
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