VizSnippets: Compressing Visualization Bundles Into Representative Previews for Browsing Visualization Collections
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Visualization collections, accessed by platforms such as Tableau Online or Power Bl, are used by millions of people to share and access diverse analytical knowledge in the form of interactive visualization bundles. Result snippets, compact previews of these bundles, are presented to users to help them identify relevant content when browsing collections. Our engagement with Tableau product teams and review of existing snippet designs on five platforms showed us that current practices fail to help people judge the relevance of bundles because they include only the title and one image. Users frequently need to undertake the time-consuming endeavour of opening a bundle within its visualization system to examine its many views and dashboards. In response, we contribute the first systematic approach to visualization snippet design. We propose a framework for snippet design that addresses eight key challenges that we identify. We present a computational pipeline to compress the visual and textual content of bundles into representative previews that is adaptive to a provided pixel budget and provides high information density with multiple images and carefully chosen keywords. We also reflect on the method of visual inspection through random sampling to gain confidence in model and parameter choices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it