Tableau Software (United States)

We reflect on an evaluation of an immersive analytics application (Tableau for visionOS) conducted at a large enterprise business intelligence (BI) conference. Conducting a study in such a context offered an opportunistic setting to gather diverse feedback. However, this setting also highlighted the challenge of evaluating usability while also assessing potential utility, as feedback straddled between the novelty of the experience and the practicality of the application in participants' analytical workflows. This formative evaluation with 22 participants allowed us to gather insights with respect to the usability of Tableau for visionOS, along with broader perspectives on the potential for head-mounted displays (HMDs) to promote new ways to engage with BI data. Our experience suggests a need for new evaluation considerations that integrate qualitative and quantitative measures and account for unique interaction patterns with 3D representations and interfaces accessible via an HMD. Overall, we contribute an enterprise perspective on evaluation methodologies for immersive analytics.

Integrating textual content, such as titles, annotations, and captions, with visualizations facilitates comprehension and takeaways during data exploration. Yet current tools often lack mechanisms for integrating meaningful long-form prose with visual data. This paper introduces DASH, a bimodal data exploration tool that supports integrating semantic levels into the interactive process of visualization and text-based analysis. DASH operationalizes a modified version of Lundgard et al.’s semantic hierarchy model that catego-rizes data descriptions into four levels ranging from basic encodings to high-level insights. By leveraging this structured semantic level framework and a large language model’s text generation capabilities, DASH enables the creation of data-driven narratives via drag-and-drop user interaction. Through a preliminary user evaluation, we discuss the utility of DASH’s text and chart integration capabilities when participants perform data exploration with the tool.

Figure 1: Beginning with the Dashboard in the top right (containing components 1 -4), the EMOJI ENCODER is an interactive chart authoring interface (1) for Tableau that generates emoji representations based on field names and the values of categorical fields. In this example, a Data Table ( 2) contains flood risk values across the Netherlands along with the number and type of employees in each province. In the expanded inspector panel (1a), the author evaluates and selects from emoji recommendations for fields and values. For instance (*), and are among the recommendations for the % cities / towns at risk of rising water levels field, for which the author has selected a diverging emoji scale for its values: . Once satisfied with an emoji palette, the author specifies (b) the type and parameters of a unit chart (c) or time series chart (Fig. 2 ). The completed chart can appear alongside other charts in the dashboard (3, 4) or be copied and pasted for text-based communication, such as in a Slack Message (5). See the supplemental video to see each chart construction step.

Cet article montre comment des acteurs réunis sous la bannière de la ceinture verte de Fennoscandie – la Greenbelt of Fennoscandia (GBF) – ont combiné des discours à propos de l’environnement et des relations internationales pour influer sur la politique de conservation de la nature dans la Russie post-soviétique. Fondé sur une recherche documentaire et près de soixante-dix entretiens, il retrace comment le concept de GBF a motivé et justifié l’ouverture d’un front écologique le long des frontières entre la Russie, la Finlande et la Norvège au nom de la protection d’une nature qui serait restée intacte du fait des restrictions d’accès imposées durant la guerre froide. Adoptant une perspective de géopolitique critique, il se penche sur la façon dont la conservation de l’environnement est mise au service de la politique de voisinage européenne, qui vise à faire de la Russie un partenaire diplomatique privilégié en l’aidant à opérer une transition vers le modèle de la démocratie libérale.

The management of supply chains is increasingly complex. This study provides a comparative analysis of the cost-benefit analysis for managing various risks. It identifies the financial implications of leveraging artificial intelligence in supply chains to better address risk. Empirical results show a business case for managing some sources of risk more proactively facilitated through predictive modeling techniques offered by AI. Across investigation streams, the use of AI results in an average total cost saving ranging from 41,254 to 4,099,617. Findings from our research can be used to inform managers and theorists about the implications of integrating AI technologies to manage risks in the supply chain. Our work also highlights areas for future research. Given the growing interest in studying sub-second forecasting, our research could be a point of departure for future investigations aimed at considering the impact of forecasting horizons such as an intra-day basis. We formulate a conceptual framework that considers how and to what extent performance evaluation metrics vary according to differences in the fidelity of predictive models and factor importance for identifying risks. We also utilize a mixed-method approach to demonstrate the applicability of our ideas in practice. Our results illustrate the financial implications of integrating AI predictive tools with business processes. Results suggest that real-world companies can circumvent inefficiencies associated with trying to manage many classes of risk via the use of AI-enhanced predictive analytics. As managers need to justify investment to top management, our work supports decision-making by providing a means of conducting a trade-off analysis at the tactical level.

Search and information retrieval systems are becoming more expressive in interpreting user queries beyond the traditional weighted bag-of-words model of document retrieval. For example, searching for a flight status or a game score returns a dynamically generated response along with supporting, pre-authored documents contextually relevant to the query. In this paper, we extend this hybrid search paradigm to data repositories that contain curated data sources and visualization content. We introduce a semantic search interface, Olio, that provides a hybrid set of results comprising both auto-generated visualization responses and pre-authored charts to blend analytical question-answering with content discovery search goals. We specifically explore three search scenarios - question-and-answering, exploratory search, and design search over data repositories. The interface also provides faceted search support for users to refine and filter the conventional best-first search results based on parameters such as author name, time, and chart type. A preliminary user evaluation of the system demonstrates that Olio’s interface and the hybrid search paradigm collectively afford greater expressivity in how users discover insights and visualization content in data repositories.

The devastating fire on January 9, 2022, led to the death of 8 children and 9 adults in New York City's Bronx borough. Previous reports have suggested that heating complaints in low socioeconomic Black/Latinx communities are frequently ignored. This trend suggests the existence of housing inequities and landlord negligence, which may lead to higher rates of residential fires in the Bronx and other low-income neighborhoods. However, this assertion has yet to be scientifically investigated. Two datasets (New York City Open Data Portal Fire Incident Dispatch and 311 Heat/Hot Water Complaints) were merged to determine the frequency of heating complaints and structural fires per month among community districts in New York City between 2017 and 2022. The primary outcome was structural fires per month which was modeled using a mixed effects multivariable regression allowing random intercepts for individual community districts. Within New York City's 59 community districts, 3,877 heating complaints were filed against 3,989 structural fires during the study period. The mixed effects model demonstrated a significant relationship between heat complaints and frequency of structural fires (coefficient 0.013, 95% confidence interval 0.012-0.014, P < .001). For the decennial census year 2020, the mixed effects model demonstrated a significant association between heat complaints and proportion of non-Hispanic, Black residents (coefficient 0.493, 95% confidence interval 0.330-0.657, P < .001). This highlights a trend in marginalized racial/ethnic communities, where unresolved heating complaints may force residents to resort to dangerous heating practices, inadvertently leading to fires and morbidity/mortality.

A common research process in visualization is for visualization researchers to collaborate with domain experts to solve particular applied data problems. While there is existing guidance and expertise around how to structure collaborations to strengthen research contributions, there is comparatively little guidance on how to navigate the implications of, and power produced through the socio-technical entanglements of collaborations. In this paper, we qualitatively analyze reflective interviews of past participants of collaborations from multiple perspectives: visualization graduate students, visualization professors, and domain collaborators. We juxtapose the perspectives of these individuals, revealing tensions about the tools that are built and the relationships that are formed — a complex web of competing motivations. Through the lens of matters of care, we interpret this web, concluding with considerations that both trouble and necessitate reformation of current patterns around collaborative work in visualization design studies to promote more equitable, useful, and care-ful outcomes.

A common research process in visualization is for visualization researchers to collaborate with domain experts to solve particular applied data problems. While there is existing guidance and expertise around how to structure collaborations to strengthen research contributions, there is comparatively little guidance on how to navigate the implications of, and power produced through the socio-technical entanglements of collaborations. In this paper, we qualitatively analyze reflective interviews of past participants of collaborations from multiple perspectives: visualization graduate students, visualization professors, and domain collaborators. We juxtapose the perspectives of these individuals, revealing tensions about the tools that are built and the relationships that are formed — a complex web of competing motivations. Through the lens of matters of care, we interpret this web, concluding with considerations that both trouble and necessitate reformation of current patterns around collaborative work in visualization design studies to promote more equitable, useful, and care-ful outcomes.

Binning is applied to categorize data values or to see distributions of data. Existing binning algorithms often rely on statistical properties of data. However, there are semantic considerations for selecting appropriate binning schemes. Surveys, for instance, gather respon-dent data for demographic-related questions such as age, salary, number of employees, etc., that are bucketed into defined semantic categories. In this paper, we leverage common semantic categories from survey data and Tableau Public visualizations to identify a set of semantic binning categories. We employ these semantic binning categories in Oscar: a method for automatically selecting bins based on the inferred semantic type of the field. We conducted a crowdsourced study with 120 participants to better understand user preferences for bins generated by Oscar vs. binning provided in Tableau. We find that maps and histograms using binned values generated by Oscar are preferred by users as compared to binning schemes based purely on the statistical properties of the data.