All

Papers

Browse and search HCI research papers from All

Active Filters

Institution: Tableau

33 results

Criticality: Scaffolding Decision-Making with Interactive Critical Thinking and Evidence-Based Reasoning Traces

Decision-making requires examining underlying assumptions and concepts, considering diverse perspectives, and weighing potential consequences with clear, accurate reasoning. Recent large language models (LLMs) show promise for assisting decision-makers by combining reasoning capabilities with the ability to retrieve r…

Minsuk Chang et al.Georgia Institute of Technology

"I Need to Find That One Chart": How Data Workers Navigate, Summarize and Communicate Analytical Conversations

Conversational interfaces are increasingly used for data analysis, enabling data workers to express complex analytical intents in natural language. Yet, these interactions unfold as long, linear transcripts that are misaligned with the iterative, nonlinear nature of real-world analyses. Revisiting and summarizing conv…

Ken Gu et al.University Of Washington

Lexara: A User-Centered Toolkit for Evaluating Large Language Models for Conversational Visual Analytics

Large Language Models (LLMs) are transforming Conversational Visual Analytics (CVA) by enabling data analysis through natural language. However, evaluating LLMs for CVA remains a challenge: requiring programming expertise, overlooking real-world complexity, and lacking interpretable metrics for multi-format (visualiza…

Srishti Palani et al.Salesforce

To Search or To Gen? Design Dimensions Integrating Web Search and Generative AI in Programmers' Information-Seeking Process

Programmers now use both generative AI (GenAI) and traditional web search for information-seeking, yet how these tools are used individually or in combination remains unclear. To answer this, we conducted a multi-phase investigation, including retrospective interviews to identify foraging behaviours and challenges and…

Ryan Yen et al.University of Waterloo

AdRecommended

Learn AI Coding at CodeNow

Structured lessons, hands-on projects, and continuous updates for people bringing AI into real development work.

Explore Now

Pluto: Authoring Semantically Aligned Text and Charts for Data-Driven Communication

Textual content (including titles, annotations, and captions) plays a central role in helping readers understand a visualization by emphasizing, contextualizing, or summarizing the depicted data. Yet, existing visualization tools provide limited support for jointly authoring the two modalities of text and visuals such…

Arjun Srinivasan et al.Tableau

Plume: Scaffolding Text Composition in Dashboards

Text in dashboards plays multiple critical roles, including providing context, offering insights, guiding interactions, and summarizing key information. Despite its importance, most dashboarding tools focus on visualizations and offer limited support for text authoring. To address this gap, we developed Plume, a syste…

Maxim Lisnic et al.University of Utah

AI-Enabled Conversational Journaling for Advancing Parkinson's Disease Symptom Tracking

Journaling plays a crucial role in managing chronic conditions by allowing patients to document symptoms and medication intake, providing essential data for long-term care. While valuable, traditional journaling methods often rely on static, self-directed entries, lacking interactive feedback and real-time guidance. T…

Mashrur Rashik et al.University of Massachusetts

Jupybara: Operationalizing a Design Space for Actionable Data Analysis and Storytelling with LLMs

Mining and conveying actionable insights from complex data is a key challenge of exploratory data analysis (EDA) and storytelling. To address this challenge, we present a design space for actionable EDA and storytelling. Synthesizing theory and expert interviews, we highlight how semantic precision, rhetorical persuas…

Huichen Will Wang et al.University Of Washington

SlopeSeeker: A Search Tool for Exploring a Dataset of Quantifiable Trends

Natural language and search interfaces intuitively facilitate data exploration and provide visualization responses to diverse analytical queries based on the underlying datasets. However, these interfaces often fail to interpret more complex analytical intents, such as discerning subtleties and quantifiable difference…

Alexander Bendeck et al.Georgia Institute of Technology

Olio: A Semantic Search Interface for Data Repositories

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…

Vidya Setlur et al.Tableau

Troubling Collaboration: Matters of Care for Visualization Design Study

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…

Derya Akbaba et al.Linkoping University

Exploring Chart Question Answering for Blind and Low Vision Users

Data visualizations can be complex or involve numerous data points, making them impractical to navigate using screen readers alone. Question answering (QA) systems have the potential to support visualization interpretation and exploration without overwhelming blind and low vision (BLV) users. To investigate if and how…

Jiho Kim et al.University of Wisconsin - Madison

Tracing and Visualizing Human-ML/AI Collaborative Processes through Artifacts of Data Work

Automated Machine Learning (AutoML) technology can lower barriers in data work yet still requires human intervention to be functional. However, the complex and collaborative process resulting from humans and machines trading off work makes it difficult to trace what was done, by whom (or what), and when. In this resea…

Jen Rogers et al.Scientific Computing and Imaging Institute

Recommendations for Visualization Recommendations: Exploring Preferences and Priorities in Visualization Recommendations for Public Health

The promise of visualization recommendation systems is that analysts will be automatically provided with relevant and high-quality visualizations that will reduce the work of manual exploration or chart creation. However, little research to date has focused on what analysts \textit{value} in \revised{the design of} vi…

Calvin S. Bao et al.University Of Maryland

How do you Converse with an Analytical Chatbot? Revisiting Gricean Maxims for Designing Analytical Conversational Behavior

Chatbots have garnered interest as conversational interfaces for a variety of tasks. While general design guidelines exist for chatbot interfaces, little work explores analytical chatbots that support conversing with data. We explore Gricean Maxims to help inform the basic design of effective conversational interactio…

Vidya Setlur et al.Tableau

Snowy: Recommending Utterances for Conversational Visual Analysis

Natural language interfaces (NLIs) have become a prevalent medium for conducting visual data analysis, enabling people with varying levels of analytic experience to ask questions of and interact with their data. While there have been notable improvements with respect to language understanding capabilities in these sys…

Arjun Srinivasan et al.Tableau

Collecting and Characterizing Natural Language Utterances for Specifying Data Visualizations

Natural language interfaces (NLIs) for data visualization are becoming increasingly popular both in academic research and in commercial software. Yet, there is a lack of empirical understanding of how people specify visualizations through natural language. We conducted an online study (N = 102), showing participants a…

Arjun Srinivasan et al.Tableau

User Ex Machina : Simulation as a Design Probe in Human-in-the-Loop Text Analytics

Topic models are widely used analysis techniques for clustering documents and surfacing thematic elements of text corpora. These models remain challenging to optimize and often require a ``human-in-the-loop'' approach where domain experts use their knowledge to steer and adjust. However, the fragility, incompleteness,…

Anamaria Crisan et al.Tableau

Data@Hand: Fostering Visual Exploration of Personal Data on Smartphones Leveraging Speech and Touch Interaction

Most mobile health apps employ data visualization to help people view their health and activity data, but these apps provide limited support for visual data exploration. Furthermore, despite its huge potential benefits, mobile visualization research in the personal data context is sparse. This work aims to empower peo…

Young-Ho Kim et al.NAVER

Fits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loop

AutoML systems can speed up routine data science work and make machine learning available to those without expertise in statistics and computer science. These systems have gained traction in enterprise settings where pools of skilled data workers are limited. In this study, we conduct interviews with 29 individuals fr…

Anamaria Crisan et al.Tableau

Paper Title	Authors	Research Topics	Paper Database	Year
Criticality: Scaffolding Decision-Making with Interactive Critical Thinking and Evidence-Based Reasoning Traces Decision-making requires examining underlying assumptions and concepts, considering diverse perspectives, and weighing potential consequences with clear, accurate reasoning. Recent large language models (LLMs) show promise for assisting decision-makers by combining reasoning capabilities with the ability to retrieve r…	MC Minsuk Chang et al.Georgia Institute of Technology
emoji_events "I Need to Find That One Chart": How Data Workers Navigate, Summarize and Communicate Analytical Conversations Conversational interfaces are increasingly used for data analysis, enabling data workers to express complex analytical intents in natural language. Yet, these interactions unfold as long, linear transcripts that are misaligned with the iterative, nonlinear nature of real-world analyses. Revisiting and summarizing conv…	KG Ken Gu et al.University Of Washington
Lexara: A User-Centered Toolkit for Evaluating Large Language Models for Conversational Visual Analytics Large Language Models (LLMs) are transforming Conversational Visual Analytics (CVA) by enabling data analysis through natural language. However, evaluating LLMs for CVA remains a challenge: requiring programming expertise, overlooking real-world complexity, and lacking interpretable metrics for multi-format (visualiza…	SP Srishti Palani et al.Salesforce
To Search or To Gen? Design Dimensions Integrating Web Search and Generative AI in Programmers' Information-Seeking Process Programmers now use both generative AI (GenAI) and traditional web search for information-seeking, yet how these tools are used individually or in combination remains unclear. To answer this, we conducted a multi-phase investigation, including retrospective interviews to identify foraging behaviours and challenges and…	RY Ryan Yen et al.University of Waterloo
AdRecommended Learn AI Coding at CodeNow Structured lessons, hands-on projects, and continuous updates for people bringing AI into real development work. Explore Now
Pluto: Authoring Semantically Aligned Text and Charts for Data-Driven Communication Textual content (including titles, annotations, and captions) plays a central role in helping readers understand a visualization by emphasizing, contextualizing, or summarizing the depicted data. Yet, existing visualization tools provide limited support for jointly authoring the two modalities of text and visuals such…	AS Arjun Srinivasan et al.Tableau
Plume: Scaffolding Text Composition in Dashboards Text in dashboards plays multiple critical roles, including providing context, offering insights, guiding interactions, and summarizing key information. Despite its importance, most dashboarding tools focus on visualizations and offer limited support for text authoring. To address this gap, we developed Plume, a syste…	ML Maxim Lisnic et al.University of Utah
emoji_events AI-Enabled Conversational Journaling for Advancing Parkinson's Disease Symptom Tracking Journaling plays a crucial role in managing chronic conditions by allowing patients to document symptoms and medication intake, providing essential data for long-term care. While valuable, traditional journaling methods often rely on static, self-directed entries, lacking interactive feedback and real-time guidance. T…	MR Mashrur Rashik et al.University of Massachusetts
Jupybara: Operationalizing a Design Space for Actionable Data Analysis and Storytelling with LLMs Mining and conveying actionable insights from complex data is a key challenge of exploratory data analysis (EDA) and storytelling. To address this challenge, we present a design space for actionable EDA and storytelling. Synthesizing theory and expert interviews, we highlight how semantic precision, rhetorical persuas…	HW Huichen Will Wang et al.University Of Washington
SlopeSeeker: A Search Tool for Exploring a Dataset of Quantifiable Trends Natural language and search interfaces intuitively facilitate data exploration and provide visualization responses to diverse analytical queries based on the underlying datasets. However, these interfaces often fail to interpret more complex analytical intents, such as discerning subtleties and quantifiable difference…	AB Alexander Bendeck et al.Georgia Institute of Technology
Olio: A Semantic Search Interface for Data Repositories 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…	VS Vidya Setlur et al.Tableau
Troubling Collaboration: Matters of Care for Visualization Design Study 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…	DA Derya Akbaba et al.Linkoping University
Exploring Chart Question Answering for Blind and Low Vision Users Data visualizations can be complex or involve numerous data points, making them impractical to navigate using screen readers alone. Question answering (QA) systems have the potential to support visualization interpretation and exploration without overwhelming blind and low vision (BLV) users. To investigate if and how…	JK Jiho Kim et al.University of Wisconsin - Madison
emoji_events Tracing and Visualizing Human-ML/AI Collaborative Processes through Artifacts of Data Work Automated Machine Learning (AutoML) technology can lower barriers in data work yet still requires human intervention to be functional. However, the complex and collaborative process resulting from humans and machines trading off work makes it difficult to trace what was done, by whom (or what), and when. In this resea…	JR Jen Rogers et al.Scientific Computing and Imaging Institute
Recommendations for Visualization Recommendations: Exploring Preferences and Priorities in Visualization Recommendations for Public Health The promise of visualization recommendation systems is that analysts will be automatically provided with relevant and high-quality visualizations that will reduce the work of manual exploration or chart creation. However, little research to date has focused on what analysts \textit{value} in \revised{the design of} vi…	CB Calvin S. Bao et al.University Of Maryland
How do you Converse with an Analytical Chatbot? Revisiting Gricean Maxims for Designing Analytical Conversational Behavior Chatbots have garnered interest as conversational interfaces for a variety of tasks. While general design guidelines exist for chatbot interfaces, little work explores analytical chatbots that support conversing with data. We explore Gricean Maxims to help inform the basic design of effective conversational interactio…	VS Vidya Setlur et al.Tableau
Snowy: Recommending Utterances for Conversational Visual Analysis Natural language interfaces (NLIs) have become a prevalent medium for conducting visual data analysis, enabling people with varying levels of analytic experience to ask questions of and interact with their data. While there have been notable improvements with respect to language understanding capabilities in these sys…	AS Arjun Srinivasan et al.Tableau
Collecting and Characterizing Natural Language Utterances for Specifying Data Visualizations Natural language interfaces (NLIs) for data visualization are becoming increasingly popular both in academic research and in commercial software. Yet, there is a lack of empirical understanding of how people specify visualizations through natural language. We conducted an online study (N = 102), showing participants a…	AS Arjun Srinivasan et al.Tableau
User Ex Machina : Simulation as a Design Probe in Human-in-the-Loop Text Analytics Topic models are widely used analysis techniques for clustering documents and surfacing thematic elements of text corpora. These models remain challenging to optimize and often require a ``human-in-the-loop'' approach where domain experts use their knowledge to steer and adjust. However, the fragility, incompleteness,…	AC Anamaria Crisan et al.Tableau
emoji_events Data@Hand: Fostering Visual Exploration of Personal Data on Smartphones Leveraging Speech and Touch Interaction Most mobile health apps employ data visualization to help people view their health and activity data, but these apps provide limited support for visual data exploration. Furthermore, despite its huge potential benefits, mobile visualization research in the personal data context is sparse. This work aims to empower peo…	YK Young-Ho Kim et al.NAVER
emoji_events Fits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loop AutoML systems can speed up routine data science work and make machine learning available to those without expertise in statistics and computer science. These systems have gained traction in enterprise settings where pools of skilled data workers are limited. In this study, we conduct interviews with 29 individuals fr…	AC Anamaria Crisan et al.Tableau