Handling Imprecise and Uncertain Engineering Information in IDEF1X and Relational Data Models
Bibliographic record
Abstract
Database modeling of engineering information is crucial for constructing manufacturing systems because current manufacturing industries are typically information-based enterprises and information systems have become their nervous center. Engineering information can be modeled at two levels: conceptual data model and logical database model. Generally a conceptual data model is designed and then the designed conceptual data model will be transformed into the chosen logical database schema. Imprecise and uncertain information, however, is generally involved in many engineering activities and imprecise and uncertain engineering information are represented by fuzzy sets. Nowadays relational databases are still the most useful database product and IDEF1X is most useful for logical database design of relational databases in engineering. So in this paper, we focus on fuzzy data modeling in IDEF1X and relational databases. The formal approaches to mapping fuzzy IDEF1X models to fuzzy relational database schemes are hereby developed.
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How this classification was reachedexpand
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 | 0.006 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".