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.

RIS

@ARTICLE{Bra ̧soveanu2017,
title = {Visualizing statistical linked knowledge for decision support},
author = {Adrian M.P. Bra ̧soveanu and Reka Marta Sabou and Alexander Hubmann-Haidvogel and Arno Scharl and Daniel Fischl},
year = {2017},
doi = {http://dx.doi.org/10.3233/SW-160225},
url = {https://content.iospress.com/articles/semantic-web/sw225},
volume = {8},
number = {1},
language = {EN},
pages = {113-137},
journal = {Semantic Web},
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.},
}