Abstract
We present a new approach to visualizing data that is well-suited
for personal and casual applications. The idea is to map the data to
another dataset that is already familiar to the user, and then rely
on their existing knowledge to illustrate relationships in the data.
We construct the map by preserving pairwise distances or by maintaining relative values of specific data attributes. This metaphorical
mapping is very flexible and allows us to adapt the visualization to
its application and target audience. We present several examples
where we map data to different domains and representations. This
includes mapping data to cat images, encoding research interests
with neural style transfer and representing movies as stars in the
night sky. Overall, we find that although metaphors are not as accurate as the traditional techniques, they can help design engaging
and personalized visualizations.
for personal and casual applications. The idea is to map the data to
another dataset that is already familiar to the user, and then rely
on their existing knowledge to illustrate relationships in the data.
We construct the map by preserving pairwise distances or by maintaining relative values of specific data attributes. This metaphorical
mapping is very flexible and allows us to adapt the visualization to
its application and target audience. We present several examples
where we map data to different domains and representations. This
includes mapping data to cat images, encoding research interests
with neural style transfer and representing movies as stars in the
night sky. Overall, we find that although metaphors are not as accurate as the traditional techniques, they can help design engaging
and personalized visualizations.
| Original language | English |
|---|---|
| Title of host publication | CHI Conference on Human Factors in Computing Systems Extended Abstracts |
| Place of Publication | New York, NY |
| Publisher | ACM Press Digital Library |
| Pages | 1-10 |
| Number of pages | 10 |
| ISBN (Print) | 978-1-4503-9156-6 |
| DOIs | |
| Publication status | Published - 28-Apr-2022 |
| Event | CHI '22: CHI Conference on Human Factors in Computing Systems - New Orleans, LA, United States Duration: 29-Apr-2022 → 5-May-2022 |
Conference
| Conference | CHI '22 |
|---|---|
| Country/Territory | United States |
| City | New Orleans, LA |
| Period | 29/04/2022 → 05/05/2022 |