Graph visualization in computer-aided design: An exploration of alternative representations for GenerativeComponentsTM Symbolic View
Bibliographic record
Abstract
In this paper we explore graph models used to illustrate the relationships between elements of designs in computer-aided design (CAD) systems. We discuss common limitations and ways to make such representations more usable and interactive. In order to study common problems of symbolic representations in CAD systems, we conducted a survey of a number of CAD applications that employ graph representations in their interface and provided comparative analysis of the properties of graph representations in these systems. As a case study we used Bentley GenerativeComponentsTM (GC) system - a parametric CAD application that uses graph (“symbolic”) view to visualize the structure of design. We conducted series of interviews with expert GC users that revealed many limitations of the GC symbolic view. To address these limitations, we developed alternative representations of symbolic view that aim at enhancing user experience with the system and reviewed these with expert GC users. As a result of our study, we developed a set of interactive prototypes using SHriMP1 visualization tool and Processing programming language. These provide improved ways of user interaction with symbolic representation, including better readability of the graph and, as a result, an improved support for design model analysis.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".