EuroVisSTAR2017
Permanent URI for this collection
Barcelona, Spain, 12 - 16 June 2017
ST1
The State-of-the-Art in Predictive Visual Analytics
] [
]
Yafeng Lu, Rolando Garcia, Brett Hansen, Michael Gleicher, and Ross Maciejewski
ST2
Social Media Visual Analytics
Siming Chen, Lijing Lin, and Xiaoru Yuan
ST3
Survey of Surveys (SoS) - Mapping The Landscape of Survey Papers in Information
Visualization
Liam McNabb and Robert S. Laramee
State of the Art in Edge and Trail Bundling Techniques
Antoine Lhuillier, Christophe Hurter, and Alex Telea
ST4
STAR: Visual Computing in Materials Science
Christoph Heinzl and Stefan Stappen
Browse
Recent Submissions
Item EuroVis 2017 - STARs: Frontmatter(Eurographics Association, 2017) Meyer, Miriah; Takahashi, Shigeo; Vilanova, Anna;Item The State-of-the-Art in Predictive Visual Analytics(The Eurographics Association and John Wiley & Sons Ltd., 2017) Lu, Yafeng; Garcia, Rolando; Hansen, Brett; Gleicher, Michael; Maciejewski, Ross; Meyer, Miriah and Takahashi, Shigeo and Vilanova, AnnaPredictive analytics embraces an extensive range of techniques including statistical modeling, machine learning, and data mining and is applied in business intelligence, public health, disaster management and response, and many other fields. To date, visualization has been broadly used to support tasks in the predictive analytics pipeline. Primary uses have been in data cleaning, exploratory analysis, and diagnostics. For example, scatterplots and bar charts are used to illustrate class distributions and responses. More recently, extensive visual analytics systems for feature selection, incremental learning, and various prediction tasks have been proposed to support the growing use of complex models, agent-specific optimization, and comprehensive model comparison and result exploration. Such work is being driven by advances in interactive machine learning and the desire of end-users to understand and engage with the modeling process. In this state-of-the-art report, we catalogue recent advances in the visualization community for supporting predictive analytics. First, we define the scope of predictive analytics discussed in this article and describe how visual analytics can support predictive analytics tasks in a predictive visual analytics (PVA) pipeline. We then survey the literature and categorize the research with respect to the proposed PVA pipeline. Systems and techniques are evaluated in terms of their supported interactions, and interactions specific to predictive analytics are discussed. We end this report with a discussion of challenges and opportunities for future research in predictive visual analytics.Item Social Media Visual Analytics(The Eurographics Association and John Wiley & Sons Ltd., 2017) Chen, Siming; Lin, Lijing; Yuan, Xiaoru; Meyer, Miriah and Takahashi, Shigeo and Vilanova, AnnaWith the development of social media (e.g. Twitter, Flickr, Foursquare, Sina Weibo, etc.), a large number of people are now using them and post microblogs, messages and multi-media information. The everyday usage of social media results in big open social media data. The data offer fruitful information and reflect social behaviors of people. There is much visualization and visual analytics research on such data. We collect state-of-the-art research and put it into three main categories: social network, spatial temporal information and text analysis. We further summarize the visual analytics pipeline for the social media, combining the above categories and supporting complex tasks. With these techniques, social media analytics can apply to multiple disciplines. We summarize the applications and public tools to further investigate the challenges and trends.Item Survey of Surveys (SoS) - Mapping The Landscape of Survey Papers in Information Visualization(The Eurographics Association and John Wiley & Sons Ltd., 2017) McNabb, Liam; Laramee, Robert S.; Meyer, Miriah and Takahashi, Shigeo and Vilanova, AnnaInformation visualization as a field is growing rapidly in popularity since the first information visualization conference in 1995. However, as a consequence of its growth, it is increasingly difficult to follow the growing body of literature within the field. Survey papers and literature reviews are valuable tools for managing the great volume of previously published research papers, and the quantity of survey papers in visualization has reached a critical mass. To this end, this survey paper takes a quantum step forward by surveying and classifying literature survey papers in order to help researchers understand the current landscape of Information Visualization. It is, to our knowledge, the first survey of survey papers (SoS) in Information Visualization. This paper classifies survey papers into natural topic clusters which enables readers to find relevant literature and develops the first classification of classifications. The paper also enables researchers to identify both mature and less developed research directions as well as identify future directions. It is a valuable resource for both newcomers and experienced researchers in and outside the field of Information Visualization and Visual Analytics.Item State of the Art in Edge and Trail Bundling Techniques(The Eurographics Association and John Wiley & Sons Ltd., 2017) Lhuillier, Antoine; Hurter, Christophe; Telea, Alex; Meyer, Miriah and Takahashi, Shigeo and Vilanova, AnnaBundling techniques provide a visual simplification of a graph drawing or trail set, by spatially grouping similar graph edges or trails. This way, the structure of the visualization becomes simpler and thereby easier to comprehend in terms of assessing relations that are encoded by such paths, such as finding groups of strongly interrelated nodes in a graph, finding connections between spatial regions on a map linked by a number of vehicle trails, or discerning the motion structure of a set of objects by analyzing their paths. In this state of the art report, we aim to improve the understanding of graph and trail bundling via the following main contributions. First, we propose a data-based taxonomy that organizes bundling methods on the type of data they work on (graphs vs trails, which we refer to as paths). Based on a formal definition of path bundling, we propose a generic framework that describes the typical steps of all bundling algorithms in terms of high-level operations and show how existing method classes implement these steps. Next, we propose a description of tasks that bundling aims to address. Finally, we provide a wide set of example applications of bundling techniques and relate these to the above-mentioned taxonomies. Through these contributions, we aim to help both researchers and users to understand the bundling landscape as well as its technicalities.Item STAR: Visual Computing in Materials Science(The Eurographics Association and John Wiley & Sons Ltd., 2017) Heinzl, Christoph; Stappen, Stefan; Meyer, Miriah and Takahashi, Shigeo and Vilanova, AnnaVisual computing has become highly attractive for boosting research endeavors in the materials science domain. Using visual computing, a multitude of different phenomena may now be studied, at various scales, dimensions, or using different modalities. This was simply impossible before. Visual computing techniques provide novel insights in order to understand complex material systems of interest, which is demonstrated by strongly rising number of new approaches, publishing new techniques for materials analysis and simulation. Outlining the proximity of materials science and visual computing, this state of the art report focuses on the intersection of both domains in order to guide research endeavors in this field. We provide a systematic survey on the close interrelations of both fields as well as how they profit from each other. Analyzing the existing body of literature, we review the domain of visual computing supported materials science, starting with the definition of materials science as well as material systems for which visual computing is frequently used. Major tasks for visual computing, visual analysis and visualization in materials sciences are identified, as well as simulation and testing techniques, which are providing the data for the respective analyses. We reviewed the input data characteristics and the direct and derived outputs, the visualization techniques and visual metaphors used, as well as the interactions and analysis workflows employed. All our findings are finally integrated in a cumulative matrix, giving insights about the different interrelations of both domains. We conclude our report with the identification of open high level and low level challenges for future research.