A Proposal for Constructing Relational Database from Class Diagram
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Database system is important to ensure the data can be stored, updated and retrieved for future use. Data modelling using the Entity Relationship Model has been introduced more than thirty years. However, designing a good database system is still an attractive issue particularly in system analysis and design because of very hard to do consistency checking between system design and database design. In this paper, a proposal for designing a relational database system based on Object Oriented Analysis and Design is presented. The database system is created by the schema table that extract from class diagram. The rules applying in this paper is following the object oriented concept. It is based on the relationships among the classes, multiplicity, attributes name, class name, data type and the behaviours of the classes. Beside that the user is required to insert record to accomplish a good designing the schema tables to avoid redundancy data. Finally, an automatic editor called CDGeST is proposed in order to automate the process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 |
| 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 it