A New Database Visualization Framework for the Automatic Construction of Non-standard Charts: Re-creating the Chart of Napoleon's Russian Campaign of 1812
Bibliographic record
Abstract
In the last decade, research on database visualization has made great progress in automatically constructing charts composed of standard primitive charts. The next research challenge is to automatically construct non-standard charts which cannot be simply composed with standard primitive types of charts. One typical example is the chart of Napoleon's Russian campaign of 1812. As to the challenge of automatic construction of such complex charts, we may classify conventional visualization frameworks into two categories. The first category asks users to procedurally define non-standard charts by programming. The second category asks users to declaratively define non-standard charts with their logical specification using a given library of graphical objects. Here we will propose a new visualization framework in the second category for automatically constructing non-standard charts from their logical specifications and discuss how to apply our framework to create custom-made geovisualization charts. Such a specification is described by one or more pairs of data view schemata (DVSs) and chart view schemata (CVSs). Each DVS is used for manipulating the data store in a database. Each CVS is used for defining the rendered appearances of the different chart components. Using our framework, users can easily re-create and extend such complex non-standard charts as the chart of Napoleon's campaign by simply providing their logical specifications.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".