An investigation of issues and techniques in highly interactive computational visualization
Bibliographic record
Abstract
Computers have changed the nature of data visualization in two important ways. First, computers allow increasingly large data sets to be automatically processed. Thus, much research effort has focused on increasing the speed and quality of graphical rendering, to accommodate data sets having greater cardinality, resolution, number of dimensions, etc. Second, computers enable interactive exploration of different views and depictions of the same data over time. Although interactive visualization is now a norm, we argue that strategies for easing and improving this interaction are relatively underexploited, and show this in three case studies. Each study involves the design and implementation of novel techniques to aid visualization in some application domain. We assert the usefulness of a small set of guiding design goals for interactive visualization that are: to ease input, to augment output over space by increasing and improving output in any given state, and to augment output over time by using smooth transitions between states. The utility of these goals is demonstrated, firstly, through the results of each case study driven in part by the goals, and secondly, by using the experience gained from the case studies to generate a set of concrete design guidelines that specify how to better achieve the design goals, and that can be applied in future design work. The first study considers visualization of 3D volumetric data, and uses spatial deformations to reduce occlusion and increase the visibility of internal surfaces of the data. The second involves visualizing and browsing a rooted tree, and uses a space-filling algorithm to automatically increase the number of nodes visible to the user. The third examines visualization of genealogical graphs, and develops layout techniques to avoid crowding of nodes and edge crossings. Each of the three studies pays attention to the meaningful subsets that exist within the data, and how to exploit these subsets during interaction. Each study also introduces an interaction technique enabling rapid and light-weight traversal of entire sequences of states, with visually continuous transitions across states. Following the case studies, the issues encountered are analyzed, a taxonomy of parameter manipulation is developed, and guidelines for future design are generated to help achieve the original design goals. Taken individually, each case study contributes significantly to its domain in the specific aspects it investigates. Furthermore, the relative variety in our case studies results in a broad perspective with which to analyze the issues encountered. The unified contribution of the work lies in demonstrating multiple ways of pursuing the design goals, and in the design guidelines subsequently generated that can be applied to situations beyond our cases studies.
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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 | 0.017 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".