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Visual analytics enables us to analyze huge information spaces in order to support complex decision making and data exploration. Humans play a central role in generating knowledge from the snippets of evidence emerging from visual data analysis. Although prior research provides frameworks that generalize this process, their scope is often narrowly focused(More)
—Soccer is one the most popular sports today and also very interesting from an scientific point of view. We present a system for analyzing high-frequency position-based soccer data at various levels of detail, allowing to interactively explore and analyze for movement features and game events. Our Visual Analytics method covers single-player, multi-player(More)
Visual analytics supports humans in generating knowledge from large and often complex datasets. Evidence is collected, collated and cross-linked with our existing knowledge. In the process, a myriad of analytical and visualisation techniques are employed to generate a visual representation of the data. These often introduce their own uncertainties, in(More)
Actions Figure 1: The role of uncertainty and trust along the visual analytics process related to data and analytic provenance. Uncertainty builds up from data source to the system output that is perceived by users. Human user's sensemaking involves trust in order to arrive at valid knowledge in the end. Such activities in visual analytics systems can be(More)
Figure 1: Ambient Grids example of the VAST Challenge 2011 microblog data subset. While an analyst follows the epidemic outbreak in the downtown area, she can keep track of the temporal development of the outbreak outside the viewport (to demonstrate the usefulness of this technique, we intentionally omit data within the viewport). On the left, the(More)
Figure 1. Tangible Data Analysis: The image shows in the background a multi-touch table visualizing a geographic map of the US east coast. A user is exploring census data with the system by using Sifteo Cubes as smart tangibles. ABSTRACT We present a tangible approach for exploring and comparing multi-dimensional data points collaboratively by combining(More)
We present an innovative visualization technique for the analysis of historical data. We illustrate our method with respect to a diachronic case study involving V1 word order in Icelandic. A number of interacting factors have been proposed by linguists as being determinative of matrix declarative V1. The significance of these factors in contributing to(More)
Dimensionality Reduction (DR) is a core building block in visualizing multidimensional data. For DR techniques to be useful in exploratory data analysis, they need to be adapted to human needs and domain-specific problems, ideally, interactively, and on-the-fly. Many visual analytics systems have already demonstrated the benefits of tightly integrating DR(More)
Preface This workshop aims at providing a follow-up forum to the successful first VisLR workshop at LREC 2014, which addresses visualization designers and users from computational and linguistic domains likewise. Since the last workshop, the concern with visualizing language data has further increased, as the recurrence of specialized symposia in the(More)