On type systems for object-oriented database programming languages
Bibliographic record
Abstract
The concept of an object-oriented database programming language (OODBPL) is appealing because it has the potential of combining the advantages of object orientation and database programming to yield a powerful and universal programming language design. A uniform and consistent combination of object orientation and database programming, however, is not straightforward. Since one of the main components of an object-oriented programming language is its type system, one of the first problems that arises during an OODBPL design is related to the development of a uniform, consistent, and theoretically sound type system that is sufficiently expressive to satisfy the combined needs of object orientation and database programming.The purpose of this article is to answer two questions: "What are the requirements that a modern type system for an object-oriented database programming language should satisfy?" and "Are there any type systems developed to-date that satisfy these requirements?". In order to answer the first question, we compile the set of requirements that an OODBPL type system should satisfy. We then use this set of requirements to evaluate more than 30 existing type systems. The result of this extensive analysis shows that while each of the requirements is satisfied by at least one type system, no type system satisfies all of them. It also enables identification of the mechanisms that lie behind the strengths and weaknesses of the current type systems.
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.
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.020 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".