1,721,313 research outputs found
User-uncertainty : A human-centred uncertainty taxonomy for VGI through the visual analytics workflow
Full text linkThe emergence of Web 2.0 and ubiquitous mobile platforms makes it possible to collect a vast amount of information contributed by people (VGI). For example, crowdsourcing applications collect information from domains such as biodiversity, urban planning, and risk management, and other sources such as social media connect citizens that exchange voluntarily huge amount of posts on platforms like Twitter, Flickr, and Facebook. VGI differs from data coming from sensors, simulations, and mathematical models. It is highly dependent on the human wills to share the information, and the background and knowledge of the user, which introduces uncertainy. In this paper, we explore different dimensions of VGI uncertainty from the perspective of the human that contributes with the data, as well as the technology and systems used to collect the data. Our contributions include a new taxonomy that explicitly differentiates among the uncertainty introduced by the humans, we named User-Uncertainty and analyzed it at different steps of the Visual Analytics Workflow, and several use cases that illustrate our approach for the case of User-Uncertainty coming from the producers. We conclude our paper with a discussion about the potential uses and future work to be done to understand User-Uncertainty
D5.1 Visual analytics for big mobility data
No full textnot availableThis project has received funding from the European Union’s Horizon 2020 research and innovation
programme under the Grant Agreement No 780754
D5.1 Visual analytics for big mobility data
No full textnot availableThis project has received funding from the European Union’s Horizon 2020 research and innovation
programme under the Grant Agreement No 780754
Fourth International Conference on Coordinated & Multiple Views in Exploratory Visualization (CMV'06)
No full textCMV 2006 is the 4th conference on coordinated and multiple views in exploratory visualization. The aim of this conference is to bring together top researchers in the area to demonstrate the state of the art, stimulate discussion, illuminate open research questions, and debate ideas and future opportunities. The first CMV conference was held in 2003. Over the last four years significant advances have been made in several directions, including fundamental theoretical issues, development of tools and infrastructure, usability and HCI studies, and applications of coordinated multiple visualizations to a variety of problems. One can observe a general portrait of this research domain by exploring the four volumes of the conference proceedings
State of the Art: Coordinated & Multiple Views in Exploratory Visualization
Full text linkThe area of coordinated and multiple views has been steadily developing and maturing over the past fifteen years. Some may say that it is a "solved problem', while others argue that we are only just scratching the surface of the subject. Considering merely the CMV conference series, it is clear to see that in the early years researchers were concerned with models and techniques, while in latter years authors presented more work on how to apply these ideas to different domains. It is our view that there is still much research to be done, but the subject is changing and developing as a tool for visual analytics. This paper provides the "state of the art' of CMV, it describes areas that should be developed further and looks at what the future may hold for coordinated and multiple views
Visuelle Analyse von großen Daten bewegender Autos – eine Überbrückung zwischen Thematischer Kartographie und Wissenschaftlicher Visualisierung
Full text linkThe aim of this thesis is to bridge the gaps between thematic mapping and scientific visualization and to achieve their synergetic effects for the visual analysis of big data. The author conducted a systematical comparative study of thematic cartography and scientific visualization. The results showed that these two disciplines reveal different visual analytical levels and are mutually complementary. Based on the theoretical findings, the author conducted extensive experiments of visually analyzing massive and complex real-world taxi floating car data. The results have confirmed our hypothesis that the two disciplines can achieve synergetic effects by taking advantage of their complementary characteristics.Das Ziel dieser Arbeit ist zwischen der Thematischen Kartographie und der Wissenschaftlichen Visualisierung eine Brücke zu bauen und die Synergieeffekte beider Disziplinen für die visuelle Analyse von Big Data zu gewinnen. Zu Bildung der theoretischen Grundlage findet eine systematische Vergleichsanalyse statt. Die Ergebnisse haben unterschiedliche visuell-analytische leistungen der beiden Disziplinen sowie deren komplementäre Rollen gezeigt. Die Autorin führt umfangreiche Untersuchungen der visuellen Analyse von Taxi-Bewegungsdaten durch. Die Ergebnisse haben die Ausgangshypothese bestätigt, dass die beiden Disziplinen aufgrund ihrer komplementären Eigenschaften vorteilhafte Synergieeffekte erzeugen können
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