Abstract Never before in history data has been generated and collected in such high volumes as it is today. Keeping up to date with the flood of data, using standard tools for data analysis and exploration, is fraught with difficulty. Visual analytics seeks to provide people with better and more effective ways to understand and analyze large datasets, while also enabling... Never before in history data has been generated and collected in such high volumes as it is today. Keeping up to date with the flood of data, using standard tools for data analysis and exploration, is fraught with difficulty. Visual analytics seeks to provide people with better and more effective ways to understand and analyze large datasets, while also enabling them to act upon their findings immediately. The field integrates the analytic capabilities of the computer and the abilities of the human analyst, allowing novel discoveries and empowering individuals to take control of the analytical process. In this paper we present the challenges of visual analytics and exemplify them with several application examples that illustrate the existing potential of current visual analysis techniques but also their limitations. Read More Read Less

Abstract Designing effective visual analytics systems is challenging. Not only must each component be well understood and effectively designed on its own, but each must also operate in harmony with the rest. To a large extent, the quality of the relationships among components determines how well visual analytic activities are supported. In this paper, we define the quality of interaction among... Designing effective visual analytics systems is challenging. Not only must each component be well understood and effectively designed on its own, but each must also operate in harmony with the rest. To a large extent, the quality of the relationships among components determines how well visual analytic activities are supported. In this paper, we define the quality of interaction among the components of visual analytics systems as interactivity. This paper draws on research from the areas of cognitive and perceptual psychology, human-information interaction, visualization sciences, and interaction design to examine some of the current challenges faced in discussing and characterizing interactivity. In doing so, this paper attempts to contribute to a characterization of interactivity in visual analytics. Read More Read Less

Abstract Our objective is to support reasoning tasks in heritage architecture with graphics enabling analysts to visualise and share their understanding of how, from a given set of information, alternative scenarios or evolution can be inferred. The paper comments on the nature of the cognitive processes in historical sciences, on factors that need to be weighed when interpreting sets of information,... Our objective is to support reasoning tasks in heritage architecture with graphics enabling analysts to visualise and share their understanding of how, from a given set of information, alternative scenarios or evolution can be inferred. The paper comments on the nature of the cognitive processes in historical sciences, on factors that need to be weighed when interpreting sets of information, and on the characteristics of the parameter time when depicting architectural changes. Visual solutions are illustrated and evaluated on real cases in Kraków, Poland. They help to spot where alternative explanations should be considered in order to avoid unjustified assumptions and certitudes on the evolution of artefacts. The contribution expects to demonstrate that reasoning on uncertainties in historical sciences can be fruitfully backed up by concepts and practices from the infovis community. Read More Read Less

Abstract Dynamic topography information landscapes are capable of visualizing longitudinal changes in large document repositories. Resembling tectonic processes in the natural world, dynamic rendering reflects both long-term trends and short-term fluctuations in such repositories. To visualize the rise and decay of topics, the mapping algorithm elevates and lowers related sets of concentric contour lines. Acknowledging the growing number of documents to... Dynamic topography information landscapes are capable of visualizing longitudinal changes in large document repositories. Resembling tectonic processes in the natural world, dynamic rendering reflects both long-term trends and short-term fluctuations in such repositories. To visualize the rise and decay of topics, the mapping algorithm elevates and lowers related sets of concentric contour lines. Acknowledging the growing number of documents to be processed by stateof- the-art Web intelligence applications, we present a scalable, incremental approach for generating such landscapes. The processing pipeline includes a number of sequential tasks, from crawling, filtering and pre-processing Web content to projecting, labeling and rendering the aggregated information. Processing steps central to incremental processing are found in the projection stage which consists of document clustering, cluster force-directed placement, and fast document positioning. We introduce two different positioning methods and compare them in an incremental setting using two different quality measures. The evaluation is performed on a set of approximately 5000 documents taken from the environmental blog sample of the Media Watch on Climate Change (www.ecoresearch.net/climate), a Web content aggregator about climate change and related environmental issues that serves static versions of the information landscapes presented in this paper as part of a multiple coordinated view representation. Read More Read Less