InterVis: um sistema para geração e exploração interativas de visualizações de informação
Bibliographic record
Abstract
Because of the growing amount of data available for analysis today, it is common to deal with large data sets, often too complex to be interpreted in their brute form.That is why Information Visualization techniques exist, to facilitate the analysis and interaction with data by humans through graphical abstractions.Motivated by the need to allow end users the autonomy to generate and edit visualizations, this work aims to underscore the importance of end user participation in the creation and support of these graphical abstractions of data.For this purpose, it was developed a system for interactive creation of Information Visualizations based on dynamic data, which aims to allow the final user to generate e edit visualizations according to their need and independently of the nature of the information that should be analyzed.This system was tested using the USE questionnaire, to verify whether this interactive creation of Information Visualizations, without programming, allied to the user knowledge of each application's domain, will be more efficient from the perspective of usability without significant loss of flexibility, as expected.The tests were compound of the tasks' execution by individuals of a users' group.All the users were able to conclude all tasks of creation and exploration in due time and evaluated positively the system, besides they have been suggested diverse improvement and new functionalities.It is possible to conclude that InterVis already fulfills the initial expectations of this work, although there are still points to be refined in future work.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".