51 research outputs found

    Representative factor generation for the interactive visual analysis of high-dimensional data

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    Datasets with a large number of dimensions per data item (hundreds or more) are challenging both for computational and visual analysis. Moreover, these dimensions have different characteristics and relations that result in sub-groups and/or hierarchies over the set of dimensions. Such structures lead to heterogeneity within the dimensions. Although the consideration of these structures is crucial for the analysis, most of the available analysis methods discard the heterogeneous relations among the dimensions. In this paper, we introduce the construction and utilization of representative factors for the interactive visual analysis of structures in high-dimensional datasets. First, we present a selection of methods to investigate the sub-groups in the dimension set and associate representative factors with those groups of dimensions. Second, we introduce how these factors are included in the interactive visual analysis cycle together with the original dimensions. We then provide the steps of an analytical procedure that iteratively analyzes the datasets through the use of representative factors. We discuss how our methods improve the reliability and interpretability of the analysis process by enabling more informed selections of computational tools. Finally, we demonstrate our techniques on the analysis of brain imaging study results that are performed over a large group of subjects

    Propagating Visual Designs to Numerous Plots and Dashboards

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    In the process of developing an infrastructure for providing visualization and visual analytics (VIS) tools to epidemiologists and modeling scientists, we encountered a technical challenge for applying a number of visual designs to numerous datasets rapidly and reliably with limited development resources. In this paper, we present a technical solution to address this challenge. Operationally, we separate the tasks of data management, visual designs, and plots and dashboard deployment in order to streamline the development workflow. Technically, we utilize: an ontology to bring datasets, visual designs, and deployable plots and dashboards under the same management framework; multi-criteria search and ranking algorithms for discovering potential datasets that match a visual design; and a purposely-design user interface for propagating each visual design to appropriate datasets (often in tens and hundreds) and quality-assuring the propagation before the deployment. This technical solution has been used in the development of the RAMPVIS infrastructure for supporting a consortium of epidemiologists and modeling scientists through visualization

    Visualising the uncertain in heritage collections : understanding, exploring and representing uncertainty in the First World War British Unit War Diaries

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    This paper argues that cultural heritage data is inherently ambiguous and may involve different types and levels of uncertainty. Using a variety of examples based on The National Archives (UK)’s Unit War Diaries collection unveiling stories of the British Army and its units on the Western Front in the First World War, we discuss the ways in which visualisation can help us approach heritage collections as data, enabling their visual representation in a constructive and informed way. It also aims to open up the discussion about the theoretical and methodological challenges that uncertainty, which is often hidden, can bring to the understanding of ambiguous heritage data. In brief, we discuss ways in which uncertainty appears in cultural heritage collections, either as something innate in the collections or resulting from the data extraction and narrative construction process. We identify three main types of uncertainty: inaccuracy, incompleteness and ambiguity, with the latter then subdivided into inconsistency, imprecision and non-specificity. Distinguishing, considering and quantifying these different types of uncertainty can help understand the level of confidence that we can have in narratives, source data and the extraction process. This can then enhance the discoverability of cultural heritage collections that involve high levels of uncertainty. In this way, we suggest that cultural heritage organisations should strategically focus on improving the understandability and discoverability of their digital collections by exposing and embracing uncertainty in cultural heritage collections and by innovating in its visual presentation to researchers and the public
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