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This paper describes an approach to IV that involves spatializing text content for enhanced visual browsing and analysis. The application arena is large text document corpora such as digital libraries, regulations andprocedures, archived reports, etc. The basic idea is that text content from these sources may be transformed to a spatial representation that preserves informational characteristics from the documents. The spatial representation may then be visually browsed and analyzed in ways that avoid language processing and that reduce the analysts' mental workload. The result is an interaction with text that more nearly resembles perception and action with the natural world than with the abstractions of written language.

Abstract. In today's applications data is produced at unprecedented rates. While the capacity to collect and store new data rapidly grows, the ability to analyze these data volumes increases at much lower rates. This gap leads to new challenges in the analysis process, since analysts, decision makers, engineers, or emergency response teams depend on information hidden in the data. The emerging field of visual analytics focuses on handling these massive, heterogenous, and dynamic volumes of information by integrating human judgement by means of visual representations and interaction techniques in the analysis process. Furthermore, it is the combination of related research areas including visualization, data mining, and statistics that turns visual analytics into a promising field of research. This paper aims at providing an overview of visual analytics, its scope and concepts, addresses the most important research challenges and presents use cases from a wide variety of application scenarios.

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Visual analytics has seen unprecedented growth in its first 5 years of mainstream existence. Great progress has been made in a short time, yet significant challenges must be met in the next decade to provide new technologies that will be widely accepted throughout the world. This article explains some of those challenges in an effort to provide a stimulus for research, both basic and applied, that can realize or even exceed the potential envisioned for visual analytics technologies. We start with a brief summary of the initial challenges, followed by a discussion of the initial driving domains and applications. These are followed by a selection of additional applications and domains that have been a part of recent rapid expansion of visual analytics usage. We then look at the common characteristics of several tools illustrating emerging visual analytics technologies and conclude with the top 10 challenges for the field of study. We encourage feedback and continued participation by members of the research community, the wide array of user communities and private industry.

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The term Visual Analytics has been around for almost five years by now, but still there are ongoing discussions about what it actually is and in particular what is new about it. The core of our view on Visual Analytics is the new enabling and accessible analytic reasoning interactions supported by the combination of automated and visual analysis. In this paper, we outline the scope of Visual Analytics using two problem and three methodological classes in order to work out the need for and purpose of Visual Analytics. By examples of analytic reasoning interaction, the respective advantages and disadvantages of automated and visual analysis methods are explained leading to a glimpse into the future of how Visual Analytics methods will enable us to go beyond what is possible when separately using the two methods.