PRISAD: a partitioned rendering infrastructure for scalable accordion drawing (extended version)
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
We present PRISAD, the first generic rendering infrastructure for information visualization applications that use the accordion drawing technique: rubber-sheet navigation with guaranteed visibility for marked areas of interest. Our new rendering algorithms are based on the partitioning of screen-space, which allows us to handle dense dataset regions correctly. The algorithms in previous work led to incorrect visual representations because of overculling, and to inefficiencies due to overdrawing multiple items in the same region. Our pixel-based drawing infrastructure guarantees correctness by eliminating overculling, and improves rendering performance with tight bounds on overdrawing. PRITree and PRISeq are applications built on PRISAD, with the feature sets of TreeJuxtaposer and SequenceJuxtaposer, respectively. We describe our PRITree and PRISeq dataset traversal algorithms, which are used for efficient rendering, culling, and layout of datasets within the PRISAD framework. We also discuss PRITree node marking techniques, which offer order-of-magnitude improvements to both memory and time performance versus previous range storage and retrieval techniques. Our PRITree implementation features a five-fold increase in rendering speed for non-trivial tree structures, and also reduces memory requirements in some real-world datasets by up to eight times, so we are able to handle trees of several million nodes. PRISeq renders fifteen times faster and handles datasets twenty times larger than previous work. The software is available as open source from
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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