145 research outputs found
Data Formulator: AI-powered Concept-driven Visualization Authoring
With most modern visualization tools, authors need to transform their data
into tidy formats to create visualizations they want. Because this requires
experience with programming or separate data processing tools, data
transformation remains a barrier in visualization authoring. To address this
challenge, we present a new visualization paradigm, concept binding, that
separates high-level visualization intents and low-level data transformation
steps, leveraging an AI agent. We realize this paradigm in Data Formulator, an
interactive visualization authoring tool. With Data Formulator, authors first
define data concepts they plan to visualize using natural languages or
examples, and then bind them to visual channels. Data Formulator then
dispatches its AI-agent to automatically transform the input data to surface
these concepts and generate desired visualizations. When presenting the results
(transformed table and output visualizations) from the AI agent, Data
Formulator provides feedback to help authors inspect and understand them. A
user study with 10 participants shows that participants could learn and use
Data Formulator to create visualizations that involve challenging data
transformations, and presents interesting future research directions
GraphMaps: Browsing Large Graphs as Interactive Maps
Algorithms for laying out large graphs have seen significant progress in the
past decade. However, browsing large graphs remains a challenge. Rendering
thousands of graphical elements at once often results in a cluttered image, and
navigating these elements naively can cause disorientation. To address this
challenge we propose a method called GraphMaps, mimicking the browsing
experience of online geographic maps.
GraphMaps creates a sequence of layers, where each layer refines the previous
one. During graph browsing, GraphMaps chooses the layer corresponding to the
zoom level, and renders only those entities of the layer that intersect the
current viewport. The result is that, regardless of the graph size, the number
of entities rendered at each view does not exceed a predefined threshold, yet
all graph elements can be explored by the standard zoom and pan operations.
GraphMaps preprocesses a graph in such a way that during browsing, the
geometry of the entities is stable, and the viewer is responsive. Our case
studies indicate that GraphMaps is useful in gaining an overview of a large
graph, and also in exploring a graph on a finer level of detail.Comment: submitted to GD 201
Favorite Folders: A Configurable, Scalable File Browser
Microsoft Windows Explorer, the most widely used file browser in
Microsoft Windows, shows almost all directories in the file system. However,
most users usually access only a subset of the directories in their machine. If
the file browser shows only the directories users are interested in, they can
select the directory they want more easily and quickly.
This paper introduces a configurable, scalable file system explorer that reduces
selection time by showing only the directories users want to see. We give users
an easy way to hide directories behind a special ellipsis node. In addition,
those hidden directories are one click away.
We present a preliminary field study conducted to validate the concept of
Favorite Folders and a theoretical model to predict the performance times.
Keywords: Windows Explorer, file browser, adaptive interfaces, customizable
interfaces
UMIACS-TR-2003-38
HCIL-TR-2003-1
WonderFlow: Narration-Centric Design of Animated Data Videos
Creating an animated data video enriched with audio narration takes a
significant amount of time and effort and requires expertise. Users not only
need to design complex animations, but also turn written text scripts into
audio narrations and synchronize visual changes with the narrations. This paper
presents WonderFlow, an interactive authoring tool, that facilitates
narration-centric design of animated data videos. WonderFlow allows authors to
easily specify a semantic link between text and the corresponding chart
elements. Then it automatically generates audio narration by leveraging
text-to-speech techniques and aligns the narration with an animation.
WonderFlow provides a visualization structure-aware animation library designed
to ease chart animation creation, enabling authors to apply pre-designed
animation effects to common visualization components. It also allows authors to
preview and iteratively refine their data videos in a unified system, without
having to switch between different creation tools. To evaluate WonderFlow's
effectiveness and usability, we created an example gallery and conducted a user
study and expert interviews. The results demonstrated that WonderFlow is easy
to use and simplifies the creation of data videos with narration-animation
interplay
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