Daniel Fischl received the B.S. degree in visual computing and the B.S. degree in economic sciences. He is currently working toward the M.S. degree in visual computing, focussing on graph-based visual tools such as Word Trees. He is a Researcher at the Department of New Media Technology, MODUL University, Vienna, Austria. His main research interests are information visualization, visual analytics, and human–computer interaction.
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"Visualizing statistical linked knowledge for decision support"2017 in: Semantic Web Journal. Volume: 8. Issue number: 1 Pages: 113-137
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.
Author(s): Adrian Brasoveanu, Marta Sabou, Arno Scharl, Alexander Hubmann-Haidvogel, Daniel Fischl
Publication date: 2017
Issue number: 1
Electronic version(s), related files and links: http://dx.doi.org/10.3233/SW-160225
Arno Scharl, Alexander Hubmann-Haidvogel, Alistair Jones, Daniel Fischl, Ruslan Kamolov, Albert Weichselbraun, Walter Rafelsberger"Analyzing the public discourse on works of fiction – Detection and visualization of emotion in online coverage about HBO’s Game of Thrones - Emotion and Sentiment in Social and Expressive Media"2016 in: Information Processing & Management. Volume: 52. Issue number: 1 Pages: 129-138
This paper presents a Web intelligence portal that captures and aggregates news and social media coverage about “Game of Thrones”, an American drama television series created for the HBO television network based on George R.R. Martin’s series of fantasy novels. The system collects content from the Web sites of Anglo-American news media as well as from four social media platforms: Twitter, Facebook, Google+ and YouTube. An interactive dashboard with trend charts and synchronized visual analytics components not only shows how often Game of Thrones events and characters are being mentioned by journalists and viewers, but also provides a real-time account of concepts that are being associated with the unfolding storyline and each new episode. Positive or negative sentiment is computed automatically, which sheds light on the perception of actors and new plot elements.
Author(s): Arno Scharl, Alexander Hubmann-Haidvogel, Alistair Jones, Daniel Fischl, Ruslan Kamolov, Albert Weichselbraun, Walter Rafelsberger
Publication date: 1. 2016
Issue number: 1
Electronic version(s), related files and links: http://dx.doi.org/10.1016/j.ipm.2015.02.003
Arno Scharl, David Herring, Walter Rafelsberger, Alexander Hubmann-Haidvogel, Ruslan Kamolov, Daniel Fischl, Michael Föls, A. Weichselbraun"Semantic Systems and Visual Tools to Support Environmental Communication"2015 in: IEEE Systems Journal. Pages: 1-10
Given the intense attention that environmental topics such as climate change attract in news and social media coverage, scientists and communication professionals want to know how different stakeholders perceive observable threats and policy options, how specific media channels react to new insights, and how journalists present scientific knowledge to the public. This paper investigates the potential of semantic technologies to address these questions. After summarizing methods to extract and disambiguate context information, we present visualization techniques to explore the lexical, geospatial, and relational context of topics and entities referenced in these repositories. The examples stem from the Media Watch on Climate Change, the Climate Resilience Toolkit and the NOAA Media Watch—three applications that aggregate environmental resources from a wide range of online sources. These systems not only show the value of providing comprehensive information to the public, but also have helped to develop a novel communication success metric that goes beyond bipolar assessments of sentiment.
Author(s): Arno Scharl, David Herring, Walter Rafelsberger, Alexander Hubmann-Haidvogel, Ruslan Kamolov, Daniel Fischl, Michael Föls, A. Weichselbraun
Publication date: 8. 9. 2015
Electronic version(s), related files and links: http://dx.doi.org/10.1109/JSYST.2015.2466439
"Topic Wizard - Interactive Visual Tool for Defining and Disambiguating Topics via Regular Expressions - 14th Brazilian Symposium on Human Factors in Computer Systems (IHC-2015)"2015 Pages: 510-513
Author(s): Arno Scharl, Daniel Fischl
Publication date: 2015
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