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Native game engines have long been the 3-D development platform of choice for research in mixed and augmented reality. For this reason, they have also been adopted in many immersive visualization and immersive analytics systems and toolkits. However, with the rapid improvements of WebXR and related open technologies, this choice may not always be optimal for future visualization research. In this article, we investigate common assumptions about native game engines versus WebXR and find that while native engines still have an advantage in many areas, WebXR is rapidly catching up and is superior for many immersive analytics applications.
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What if magic could be used as an effective metaphor to perform data visualization and analysis using speech and gestures while mobile and on-the-go? In this paper, we introduce WIZUALIZATION, a visual analytics system for eXtended Reality (XR) that enables an analyst to author and interact with visualizations using such a magic system through gestures, speech commands, and touch interaction. Wizualization is a rendering system for current XR headsets that comprises several components: a cross-device (or ARCANE FOCUSES) infrastructure for signalling and view control (WEAVE), a code notebook (SPELLBOOK), and a grammar of graphics for XR (OPTOMANCY). The system offers users three modes of input: gestures, spoken commands, and materials. We demonstrate Wizualization and its components using a motivating scenario on collaborative data analysis of pandemic data across time and space.
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Analyzing user behavior from usability evaluation can be a challenging and time-consuming task, especially as the number of participants and the scale and complexity of the evaluation grows. We propose UXSENSE, a visual analytics system using machine learning methods to extract user behavior from audio and video recordings as parallel time-stamped data streams. Our implementation draws on pattern recognition, computer vision, natural language processing, and machine learning to extract user sentiment, actions, posture, spoken words, and other features from such recordings. These streams are visualized as parallel timelines in a web-based front-end, enabling the researcher to search, filter, and annotate data across time and space. We present the results of a user study involving professional UX researchers evaluating user data using uxSense. In fact, we used uxSense itself to evaluate their sessions.
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The advent of low-cost, accessible, and high-performance augmented reality (AR) has shed light on a situated form of analytics where in-situ visualizations embedded in the real world can facilitate sensemaking based on the user's physical location. In this work, we identify prior literature in this emerging field with a focus on the technologies enabling such situated analytics. After collecting 47 relevant situated analytics systems, we classify them using a taxonomy of three dimensions: situating triggers, view situatedness, and data depiction. We then identify four archetypical patterns in our classification using an ensemble cluster analysis. Finally, we discuss several insights and design guidelines that we learned from our analysis.
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Immersive analytics turns the very space surrounding the user into a canvas for data analysis, supporting human cognitive abilities in myriad ways. We present the results of a design study, contextual inquiry, and longitudinal evaluation involving professional economists using a Virtual Reality (VR) system for multidimensional visualization to explore actual economic data. Results from our preregistered evaluation highlight the varied use of space depending on context (exploration vs. presentation), the organization of space to support work, and the impact of immersion on navigation and orientation in the 3D analysis space.
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Data scientists and other analytic professionals often use interactive visualization in the dissemination phase at the end of a workflow during which findings are communicated to a wider audience. Visualization scientists, however, hold that interactive representation of data can also be used during exploratory analysis itself. Since the use of interactive visualization is optional rather than mandatory, this leaves a "visualization gap" during initial exploratory analysis that is the onus of visualization researchers to fill. In this paper, we explore areas where visualization would be beneficial in applied research by conducting a design study using a novel variation on contextual inquiry conducted with professional data analysts. Based on these interviews and experiments, we propose a set of interactive initial exploratory visualization guidelines which we believe will promote adoption by this type of user.
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Olfactory feedback for analytical tasks is a virtually unexplored area in spite of the advantages it offers for information recall, feature identification, and location detection. Here we introduce the concept of information olfactation as the fragrant sibling of information visualization, and discuss how scent can be used to convey data. Building on a review of the human olfactory system and mirroring common visualization practice, we propose olfactory marks, the substrate in which they exist, and their olfactory channels that are available to designers. To exemplify this idea, we present VISCENT: A six-scent stereo olfactory display capable of conveying olfactory glyphs of varying temperature and direction, as well as a corresponding software system that integrates the display with a traditional visualization display. Finally, we present three applications that make use of the viScent system: A 2D graph visualization, a 2D line and point chart, and an immersive analytics graph visualization in 3D virtual reality. We close the paper with a review of possible extensions of viScent and applications of information olfactation for general visualization beyond the examples in this paper.
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We introduce a new source of detailed data on spending by medical condition to analyze US health care spending growth in the period 2000-14. We found that thirty conditions, which represented only 11.5 percent of all conditions studied, accounted for 42 percent of the real growth rate in per capita spending during this period, even though they accounted for only 13 percent of overall spending in 2000. Primary drivers of spending growth included the use of new technologies, a shift toward the provision of preventive-type services, and an aging and more obese population. The health benefits of many new technologies appeared to outweigh the associated expenditures on treatment, which indicates that these are cost-effective and provide a net value to society. However, while these technologies may be of value, new treatments are often more expensive than older ones.
Assuntos
Tecnologia Biomédica/economia , Efeitos Psicossociais da Doença , Atenção à Saúde/economia , Demografia/economia , Gastos em Saúde/estatística & dados numéricos , Análise Custo-Benefício , Bases de Dados Factuais , Feminino , Gastos em Saúde/tendências , Humanos , Revisão da Utilização de Seguros , Masculino , Estudos Retrospectivos , Estados UnidosRESUMO
This paper focuses on value sets as an essential component in the health analytics ecosystem. We discuss shared repositories of reusable value sets and offer recommendations for their further development and adoption. In order to motivate these contributions, we explain how value sets fit into specific analytic tasks and the health analytics landscape more broadly; their growing importance and ubiquity with the advent of Common Data Models, Distributed Research Networks, and the availability of higher order, reusable analytic resources like electronic phenotypes and electronic clinical quality measures; the formidable barriers to value set reuse; and our introduction of a concept-agnostic orientation to vocabulary collections. The costs of ad hoc value set management and the benefits of value set reuse are described or implied throughout. Our standards, infrastructure, and design recommendations are not systematic or comprehensive but invite further work to support value set reuse for health analytics. The views represented in the paper do not necessarily represent the views of the institutions or of all the co-authors.