DataPilot: Utilizing Quality and Usage Information for Subset Selection during Visual Data Preparation (original) (raw)
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MARVIS: Combining Mobile Devices and Augmented Reality for Visual Data Analysis
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Figure 1: Data visualization using mobile devices and Augmented Reality head-mounted displays: (a) Envisioned usage scenario; (b) 2D scatterplot extended with superimposed 3D trajectories/paths; (c) 3D wall visualization in AR aligned with the mobile device; (d) Use of AR for seamless display extension around a geographic map; (e) Combining visualizations with an AR view between the devices.
Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Figure 1: Our MIRIA toolkit supports the co-located, in-situ analysis of spatial interaction data by multiple users in Augmented Reality. It provides 3D visualizations, e.g., trajectories and trails, and 2D visualizations, e.g., scatterplots and heatmaps. MIRIA also supports 3D models, videos, and pictures placed in space, providing additional context to the data.
Cicero: A Declarative Grammar for Responsive Visualization
CHI Conference on Human Factors in Computing Systems
Figure desktop and mobile versions, respectively. The mobile versions of the Oil Spills case are from (1) the original article and (2) the version suggested by Hofswell et al. [13]. Full size images are included in the Supplemental Material (https://osf.io/eg4xq). 1: Thirteen responsive visualization use cases reproduced using Cicero. The blue-and gray-bordered views are the
GestureExplorer: Immersive Visualisation and Exploration of Gesture Data
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
Figure 1: GestureExplorer supports immersive exploration of gesture data. Gestures are clustered by similarity and can be spatially arranged by similarity distance to each cluster (left), or in sorted order (middle). We provide several interactive features for exploring individual gestures such as trajectory visualisation, small multiples, and animation (right).
CHI Conference on Human Factors in Computing Systems
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 value in the design of visualization recommendations. We interviewed 18 analysts in the public health sector and explored how they made sense of a popular in-domain dataset 1 in service of generating visualizations to recommend to others. We also explored how they interacted with a corpus of both automaticallyand manually-generated visualization recommendations, with the goal of uncovering how the design values of these analysts are reflected in current visualization recommendation systems. We find that analysts champion simple charts with clear takeaways that are nonetheless connected with existing semantic information or domain hypotheses. We conclude by recommending that visualization recommendation designers explore ways of integrating context and expectation into their systems. CCS CONCEPTS • Human-centered computing → Visualization design and evaluation methods; Visualization systems and tools; User studies.
CHI Conference on Human Factors in Computing Systems
Figure 1: Participants conversing with various analytical chatbot prototypes. (a) A Slack chatbot showing an interactive message with a drop-down menu to help a user refine a previous response within the conversation thread. (b) An Echo Show chatbot simulator screen showing the top 5 wineries result along with two other follow-up utterance options on the right side of the screen. (c) Interaction with an Echo chatbot. The grey text bubbles indicate voice transcripts from the participants while the blue ones are from the chatbot. Follow-up questions and feedback from the chatbot encourage conversational behavior.
CHI Conference on Human Factors in Computing Systems
This paper introduces the concept of "news informatics" to refer to journalistic presentation of big data in online sites. For users to be engaged with such data-driven public information, it is important to incorporate interactive tools so that each person can extract personally relevant information. Drawing upon a communication model of interactivity, we designed a data-rich site with three diferent types of interactive features-namely, modality interactivity, message interactivity, and source interactivity-and empirically tested their relative and combined efects on user engagement and user experience with a 2 (modality) × 3 (source) × 2 (message) feld experiment (N =166). Findings shed light on how interface designers, online news editors and journalists can maximize user engagement with data-rich news content. Certain interactivity combinations are found to be better than others, with a structural equation model (SEM) revealing the underlying theoretical mechanisms and providing implications for the design of news informatics.
Varv: Reprogrammable Interactive Software as a Declarative Data Structure
CHI Conference on Human Factors in Computing Systems
Figure 1: Varv Examples: (a) A todo list web application that is inherently extensible. Here, a basic todo list is extended with the ability to complete and delete todos by adding two new concept defnitions and new modifed template defnitions. (b) A board game toolkit that defnes abstractions for board game logic. The games "Checkers" and "Othello" were implemented with the toolkit and then merged into a new "Checkers-O-Thello" game with the addition of a short concept defnition. As Varv applications are represented as data structures, higher-level tooling can be developed including a block-based editor (right), an inspector to go from an element in the view to the corresponding template or data (context menu to the left), and a data inspector for live editing application state (middle).